Search

Causal discovery with tiered background knowledge leads to more informative estimations of the true underlying data-generating DAG. In this thesis, we develop two score-based causal discovery algorithms that incorporate tiered background knowledge: Temporal Greedy Equivalence Search (TGES) and Simple Temporal Greedy Equivalence Search (Simple TGES). To be able to define the algorithms, we introduce needed graph theory, relevant score criteria, tiered background knowledge, and Greedy Equivalence Search (GES). We prove that TGES will always estimate a tiered MPDAG and that Simple TGES is sound and complete in the limit of large sample size. The algorithms have been implemented in R. We perform a simulation study, here amongst using a new metric on the performance of causal discovery algorithms. The simulation study shows that TGES outperforms Simple TGES and GES with respect to both adjacencies and orientations of edges. The computational time of TGES is slower than GES but shows potential to become faster. We finish the thesis by discussing the findings and methods used throughout.½

Causal discovery with tiered background knowledge leads to more informative estimations of the true underlying data-generating DAG. In this thesis, we develop two score-based causal discovery algorithms that incorporate tiered background knowledge: Temporal Greedy Equivalence Search (TGES) and Simple Temporal Greedy Equivalence Search (Simple TGES). To be able to define the algorithms, we introduce needed graph theory, relevant score criteria, tiered background knowledge, and Greedy Equivalence Search (GES). We prove that TGES will always estimate a tiered MPDAG and that Simple TGES is sound and complete in the limit of large sample size. The algorithms have been implemented in R. We perform a simulation study, here amongst using a new metric on the performance of causal discovery algorithms. The simulation study shows that TGES outperforms Simple TGES and GES with respect to both adjacencies and orientations of edges. The computational time of TGES is slower than GES but shows potential to become faster. We finish the thesis by discussing the findings and methods used throughout.½

The search for causal relations from observational or experimental data is an open and pervasive problem that spans many fields of research. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is persistently desirable. In computer science courses, integrated development environments (IDE) offer students a plethora of features promising to accelerate the coding process and instill the necessary competency skills for seamless migration to industry. In this work, we introduce a mathematical definition of a coding process and apply causal discovery methods to data collected from an IDE. We approached the problem statically and dynamically and found strong evidence of a causal effect of consultations on coding competency. We set forth the groundwork for future analyses of these processes and exhibit the extendibility of this work to other disciplines., 2024-06, 2024-02-28T17:27:25Z

La nozione di intervento esterno è di fondamentale importanza negli ambiti dell'inferenza causale e della scoperta causale da combinazioni di dati osservazionali e sperimentali. Nel primo, essa costituisce uno strumento fondamentale per la definizione, identificazione e stima degli effetti causali. Nel secondo, consente di definire precisamente ciò che i dati sperimentali implicano, consentendo così il loro utilizzo per migliorare l'identificabilità delle strutture causali da stimare. Questa tesi affronta entrambi gli ambiti attraverso la lente dell'inferenza bayesiana. Il manoscritto è composto da tre capitoli, di cui il primo serve da introduzione all'ambito generale della tesi, delineandone il contesto scientifico e il contributo. I principali due capitoli scaturiscono da due progetti indipendenti, ma accomunati dall'idea di estendere la nozione di intervento esterno al fine di riflettere gli esperimenti realmente condotti nell'attività di ricerca scientifica. Nel Capitolo 2, consideriamo il caso di interventi congiunti che coinvolgono più variabili simultaneamente. In particolare, proponiamo un approccio bayesiano che, nel contesto gaussiano, unisce le metodologie di scoperta causale a quelle di stima degli effetti causali, risultando in una metodologia di Bayesian model averaging per la stima di effetti causali congiunti quando la struttura causale del processo generatore dei dati è ignota. Nel Capitolo 3, analizziamo interventi che modificando il meccanismo causale delle variabili target e che chiamiamo general interventions. In tale contesto, costruiamo una metodologia bayesiana di scoperta causale da combinazioni di dati osservazionali e sperimentali, ipotizzando che questi ultimi abbiano la loro origine in general intervention con target ignote. Inoltre, definiamo e diamo una caratterizzazione grafica del limite di identificabilità delle strutture causali e disegniamo un algoritmo MCMC per campionare dalla distribuzione a posteriori congiunta sullo, The notion of external intervention is of fundamental importance within the fields of causal inference and causal discovery from combinations of observational and experimental data. In the former, it serves as a tool for defining, identifying, and estimating causal effects. In the latter, it allows for a precise definition of what experimental data entail, enabling their usage to improve the identifiability of causal structures. This thesis addresses both aspects, investigating them within the Bayesian framework. The manuscript comprises three chapters, with the first serving as an introduction to the general scope of the thesis, outlining its scientific context and contribution. The main two chapters represent two independent projects, both driven by the objective of broadening the notion of external intervention to reflect the diverse manipulations that scientists may actually implement in their experiments. In Chapter 2, we consider the case of joint interventions which may simultaneously affect several variables. In particular, we present a unified Bayesian approach for causal discovery and causal effect estimation in the Gaussian setting. This leads to a Bayesian model averaging strategy for estimating the joint causal effects associated with such interventions when the causal structure of the data-generating process is unknown. In Chapter 3, we instead consider interventions modifying the causal mechanisms of the intervened variables, which we call general interventions. We thus construct a Bayesian model for causal discovery from combinations of observational and experimental data originating from unknown general interventions. In addition, we provide definitions and graphical characterizations of the identifiability limit of causal structures in the new setting and devise a suitable MCMC scheme to sample from the joint posterior distribution over causal structures and unknown general interventions.

Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), BACKGROUND: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. OBJECTIVE: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. METHODS: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. RESULTS: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenat

Constraint-based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g., that the true data generating model is a Hawkes process with no latent confounders. Even when restricting attention to Hawkes processes, latent confounders are a major technical difficulty because a marginalized process will generally not be a Hawkes process itself. We introduce an expansion similar to Volterra expansions as a tool to represent marginalized intensities. Our main theoretical result is that such expansions can approximate the true marginalized intensity arbitrarily well. Based on this, we propose a test of local independence and investigate its properties in real and simulated data.

Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), BACKGROUND: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. OBJECTIVE: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. METHODS: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. RESULTS: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenat

Constraint-based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g., that the true data generating model is a Hawkes process with no latent confounders. Even when restricting attention to Hawkes processes, latent confounders are a major technical difficulty because a marginalized process will generally not be a Hawkes process itself. We introduce an expansion similar to Volterra expansions as a tool to represent marginalized intensities. Our main theoretical result is that such expansions can approximate the true marginalized intensity arbitrarily well. Based on this, we propose a test of local independence and investigate its properties in real and simulated data.

The search for causal relations from observational or experimental data is an open and pervasive problem that spans many fields of research. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is persistently desirable. In computer science courses, integrated development environments (IDE) offer students a plethora of features promising to accelerate the coding process and instill the necessary competency skills for seamless migration to industry. In this work, we introduce a mathematical definition of a coding process and apply causal discovery methods to data collected from an IDE. We approached the problem statically and dynamically and found strong evidence of a causal effect of consultations on coding competency. We set forth the groundwork for future analyses of these processes and exhibit the extendibility of this work to other disciplines., 2024-06

Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. These relationships are typically learned through performance and resource utilization metrics of the microservices in the system. To accomplish this objective, numerous RCA systems utilize statistical algorithms, specifically those falling under the category of causal discovery. These algorithms have demonstrated their utility not only in RCA systems but also in a wide range of other domains and applications. Nonetheless, there exists a research gap in the exploration of the feasibility and efficacy of multivariate time series causal discovery algorithms for deriving causal graphs within a microservice framework. By harnessing metric time series data from Prometheus and applying these algorithms, we aim to shed light on their performance in a cloudnative environment. Furthermore, we have introduced an adaptation in the form of an ensemble causal discovery algorithm. Our experimentation with this ensemble approach, conducted on datasets with known causal relationships, unequivocally demonstrates its potential in enhancing the precision of detected causal connections. Notably, our ultimate objective was to ascertain reliable causal relationships within Ericsson’s cloud-native system ’X,’ where the ground truth is unavailable. The ensemble causal discovery approach triumphs over the limitations of employing individual causal discovery algorithms, significantly augmenting confidence in the unveiled causal relationships. As a practical illustration of the utility of the ensemble causal discovery techniques, we have delved into the domain of anomaly detection. By leveraging causal graphs within our study, we have successfully applied this technique to anomaly detection withi, System för automatisk rotorsaksanalys (RCA) syftar till att effektivisera process för att identifiera den underliggande orsaken till programvarufel i komplexa molnbaserade miljöer. Dessa system använder grafliknande strukturer att representera orsakssamband mellan olika komponenter i en mjukvaruapplikation. Dessa relationer lär man sig vanligtvis genom prestanda och resursutnyttjande mätvärden för mikrotjänsterna i systemet. För att uppnå detta mål använder många RCAsystem statistiska algoritmer, särskilt de som faller under kategorin orsaksupptäckt. Dessa algoritmer har visat att de inte är användbara endast i RCA-system men även inom en lång rad andra domäner och applikationer. Icke desto mindre finns det en forskningslucka i utforskningen av genomförbarhet och effektivitet av orsaksupptäckt av multivariat tidsserie algoritmer för att härleda kausala grafer inom ett mikrotjänstramverk. Genom att utnyttja metriska tidsseriedata från Prometheus och tillämpa Dessa algoritmer strävar vi efter att belysa deras prestanda i ett moln- inhemsk miljö. Dessutom har vi infört en anpassning i formen av en ensemble kausal upptäcktsalgoritm. Vårt experiment med denna ensemblemetod, utförd på datauppsättningar med kända orsakssamband relationer, visar otvetydigt sin potential för att förbättra precisionen hos upptäckta orsakssamband. Särskilt vår ultimata Målet var att fastställa tillförlitliga orsakssamband inom Ericssons molnbaserade systemet ’X’, där grundsanningen inte är tillgänglig. De ensemble kausal discovery approach segrar över begränsningarna av att använda individuella kausala upptäcktsalgoritmer, avsevärt öka förtroendet för de avslöjade orsakssambanden. Som en praktisk illustration av nyttan av ensemblens kausal upptäcktstekniker har vi fördjupat oss i anomalidomänen upptäckt. Genom att utnyttja kausala grafer inom vår studie har vi framgångsrikt tillämpat denna teknik för att detektera anomali inom Ericsson system

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems., QC 20240625

The goal of many sciences is to find causal relationships and understand underlying mechanisms. As the golden standard for finding causal relationships, doing randomized experiments can be difficult or impossible in some applications; hence, determining underlying causal relationships purely from observational data, i.e., causal discovery, has attracted more and more attention in many domains, such as earth science, biology, and healthcare. On the one hand, computational methods of causal discovery have been developed and improved significantly in the recent three decades. On the other hand, there are still many challenges in both practice and theory to further achieve real-world impacts. This thesis aims to introduce the typical methods and challenges of causal discovery and then elaborates on the contributions of the included papers that step forward to achieve more real-world impacts for causal discovery. It mainly covers four challenges: practical issues, understanding and generalizing the restrictive assumptions, the lack of benchmark data sets, and applications of causality in machine learning topics. Each included paper contributes to one of the challenges. In the first paper, regarding causal discovery in the presence of missing data as one of the practical issues, we theoretically study the influence of missing values on causal discovery methods and then correct the errors in their results. Under mild assumptions, our proposed method provides asymptotically correct results. In the second paper, we investigate the understanding of assumptions in a class of causal discovery methods. Such methods impose substantial constraints on functional classes and distributions of causal processes for determining causal relationships; however, the constraints are restrictive and there is a lack of good understanding. Therefore, we introduce a new dynamical-system view for understanding the methods and their constraints by connecting optimal transport and causal discovery., Målet för många vetenskapsområden är att hitta orsakssamband och förstå bakomliggande mekanismer. Som den gyllene standarden för att hitta orsakssamband kan slumpmässiga experiment vara svåra eller omöjliga i vissa tillämpningar; Därför har bestämning av underliggande orsakssamband enbart från observationsdata, d.v.s. kausal upptäckt, väckt mer och mer uppmärksamhet inom många områden, såsom geovetenskap, biologi och sjukvård. Å ena sidan har beräkningsmetoder för kausal upptäckt utvecklats och förbättrats avsevärt under de senaste tre decennierna. Å andra sidan finns det fortfarande många utmaningar kvar i både praktik och teori för att ytterligare uppnå verkliga effekter. Denna avhandling syftar till att introducera de typiska metoderna och utmaningarna för kausal upptäckt och sedan utveckla bidragen från de inkluderade artiklarna som tar kliv framåt för att uppnå fler verkliga effekter för kausal upptäckt. Den täcker huvudsakligen fyra utmaningar: praktiska frågor, förståelse och generalisering av de restriktiva antagandena, bristen på uppsättningar av referensdata och tillämpningar av kausalitet i maskininlärningsområden. Varje medföljande artikel bidrar till en av utmaningarna. I den första artikeln, angående kausal upptäckt i närvaro av saknade data som en av de praktiska frågorna, studerar vi teoretiskt saknade värdens inverkan på metoder för kausal upptäckt och korrigerar sedan felen i deras resultat. Under milda antaganden ger vår föreslagna metod korrekta resultat. I den andra artikeln undersöker vi förståelsen av antaganden i en klass av kausala upptäcktsmetoder. Sådana metoder lägger betydande begränsningar på funktionella klasser och fördelningar av kausala processer för att bestämma orsakssamband; dock är begränsningarna restriktiva och det saknas god förståelse. Därför introducerar vi en ny dynamisk systemvy för att förstå metoderna och deras begränsningar genom att koppla ihop optimal transport och kausal upptäckt. Dessutom tillhandahåller vi ett kau, QC 20221217

The goal of many sciences is to find causal relationships and understand underlying mechanisms. As the golden standard for finding causal relationships, doing randomized experiments can be difficult or impossible in some applications; hence, determining underlying causal relationships purely from observational data, i.e., causal discovery, has attracted more and more attention in many domains, such as earth science, biology, and healthcare. On the one hand, computational methods of causal discovery have been developed and improved significantly in the recent three decades. On the other hand, there are still many challenges in both practice and theory to further achieve real-world impacts. This thesis aims to introduce the typical methods and challenges of causal discovery and then elaborates on the contributions of the included papers that step forward to achieve more real-world impacts for causal discovery. It mainly covers four challenges: practical issues, understanding and generalizing the restrictive assumptions, the lack of benchmark data sets, and applications of causality in machine learning topics. Each included paper contributes to one of the challenges. In the first paper, regarding causal discovery in the presence of missing data as one of the practical issues, we theoretically study the influence of missing values on causal discovery methods and then correct the errors in their results. Under mild assumptions, our proposed method provides asymptotically correct results. In the second paper, we investigate the understanding of assumptions in a class of causal discovery methods. Such methods impose substantial constraints on functional classes and distributions of causal processes for determining causal relationships; however, the constraints are restrictive and there is a lack of good understanding. Therefore, we introduce a new dynamical-system view for understanding the methods and their constraints by connecting optimal transport and causal discovery., Målet för många vetenskapsområden är att hitta orsakssamband och förstå bakomliggande mekanismer. Som den gyllene standarden för att hitta orsakssamband kan slumpmässiga experiment vara svåra eller omöjliga i vissa tillämpningar; Därför har bestämning av underliggande orsakssamband enbart från observationsdata, d.v.s. kausal upptäckt, väckt mer och mer uppmärksamhet inom många områden, såsom geovetenskap, biologi och sjukvård. Å ena sidan har beräkningsmetoder för kausal upptäckt utvecklats och förbättrats avsevärt under de senaste tre decennierna. Å andra sidan finns det fortfarande många utmaningar kvar i både praktik och teori för att ytterligare uppnå verkliga effekter. Denna avhandling syftar till att introducera de typiska metoderna och utmaningarna för kausal upptäckt och sedan utveckla bidragen från de inkluderade artiklarna som tar kliv framåt för att uppnå fler verkliga effekter för kausal upptäckt. Den täcker huvudsakligen fyra utmaningar: praktiska frågor, förståelse och generalisering av de restriktiva antagandena, bristen på uppsättningar av referensdata och tillämpningar av kausalitet i maskininlärningsområden. Varje medföljande artikel bidrar till en av utmaningarna. I den första artikeln, angående kausal upptäckt i närvaro av saknade data som en av de praktiska frågorna, studerar vi teoretiskt saknade värdens inverkan på metoder för kausal upptäckt och korrigerar sedan felen i deras resultat. Under milda antaganden ger vår föreslagna metod korrekta resultat. I den andra artikeln undersöker vi förståelsen av antaganden i en klass av kausala upptäcktsmetoder. Sådana metoder lägger betydande begränsningar på funktionella klasser och fördelningar av kausala processer för att bestämma orsakssamband; dock är begränsningarna restriktiva och det saknas god förståelse. Därför introducerar vi en ny dynamisk systemvy för att förstå metoderna och deras begränsningar genom att koppla ihop optimal transport och kausal upptäckt. Dessutom tillhandahåller vi ett kau, QC 20221217

Finding a directed acyclic graph (DAG) that best encodes the conditional inde-pendence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example, the greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing algorithms. In 2010, Studenty, Hemmecke, and Lindner introduced the char-acteristic imset (CIM) polytope, CIMp, whose vertices correspond to Markov equivalence classes, as a way of transforming causal discovery into a linear optimization problem. We show that the moves of the aforementioned algorithms are included within classes of edges of CIMp and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of CIMp. Thus, we observe that greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing all have geometric realizations as greedy edge-walks along CIMp. Furthermore, the identified edges of CIMp strictly generalize the moves of these algorithms. Exploiting this generalization, we introduce a greedy simplex-type algorithm called greedy CIM, and a hybrid variant, skeletal greedy CIM, that outperforms current competitors among hybrid and constraint-based algorithms., QC 20230425

The interdisciplinary field of knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.

Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. These relationships are typically learned through performance and resource utilization metrics of the microservices in the system. To accomplish this objective, numerous RCA systems utilize statistical algorithms, specifically those falling under the category of causal discovery. These algorithms have demonstrated their utility not only in RCA systems but also in a wide range of other domains and applications. Nonetheless, there exists a research gap in the exploration of the feasibility and efficacy of multivariate time series causal discovery algorithms for deriving causal graphs within a microservice framework. By harnessing metric time series data from Prometheus and applying these algorithms, we aim to shed light on their performance in a cloudnative environment. Furthermore, we have introduced an adaptation in the form of an ensemble causal discovery algorithm. Our experimentation with this ensemble approach, conducted on datasets with known causal relationships, unequivocally demonstrates its potential in enhancing the precision of detected causal connections. Notably, our ultimate objective was to ascertain reliable causal relationships within Ericsson’s cloud-native system ’X,’ where the ground truth is unavailable. The ensemble causal discovery approach triumphs over the limitations of employing individual causal discovery algorithms, significantly augmenting confidence in the unveiled causal relationships. As a practical illustration of the utility of the ensemble causal discovery techniques, we have delved into the domain of anomaly detection. By leveraging causal graphs within our study, we have successfully applied this technique to anomaly detection withi, System för automatisk rotorsaksanalys (RCA) syftar till att effektivisera process för att identifiera den underliggande orsaken till programvarufel i komplexa molnbaserade miljöer. Dessa system använder grafliknande strukturer att representera orsakssamband mellan olika komponenter i en mjukvaruapplikation. Dessa relationer lär man sig vanligtvis genom prestanda och resursutnyttjande mätvärden för mikrotjänsterna i systemet. För att uppnå detta mål använder många RCAsystem statistiska algoritmer, särskilt de som faller under kategorin orsaksupptäckt. Dessa algoritmer har visat att de inte är användbara endast i RCA-system men även inom en lång rad andra domäner och applikationer. Icke desto mindre finns det en forskningslucka i utforskningen av genomförbarhet och effektivitet av orsaksupptäckt av multivariat tidsserie algoritmer för att härleda kausala grafer inom ett mikrotjänstramverk. Genom att utnyttja metriska tidsseriedata från Prometheus och tillämpa Dessa algoritmer strävar vi efter att belysa deras prestanda i ett moln- inhemsk miljö. Dessutom har vi infört en anpassning i formen av en ensemble kausal upptäcktsalgoritm. Vårt experiment med denna ensemblemetod, utförd på datauppsättningar med kända orsakssamband relationer, visar otvetydigt sin potential för att förbättra precisionen hos upptäckta orsakssamband. Särskilt vår ultimata Målet var att fastställa tillförlitliga orsakssamband inom Ericssons molnbaserade systemet ’X’, där grundsanningen inte är tillgänglig. De ensemble kausal discovery approach segrar över begränsningarna av att använda individuella kausala upptäcktsalgoritmer, avsevärt öka förtroendet för de avslöjade orsakssambanden. Som en praktisk illustration av nyttan av ensemblens kausal upptäcktstekniker har vi fördjupat oss i anomalidomänen upptäckt. Genom att utnyttja kausala grafer inom vår studie har vi framgångsrikt tillämpat denna teknik för att detektera anomali inom Ericsson system

One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. Possible identifiability of SCMs given data depends on assumptions made on the noise variables and the functional classes in the SCM. For instance, in the LiNGAM model, the functional class is restricted to linear functions and the disturbances have to be non-Gaussian. In this work, we propose TSLiNGAM, a new method for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain more efficient and robust estimation of the causal structure. TSLiNGAM is justified theoretically and is studied empirically in an extensive simulation study. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency. In addition, TSLiNGAM also shows better robustness properties as it is more resilient to contamination.

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

The goal of many sciences is to find causal relationships and understand underlying mechanisms. As the golden standard for finding causal relationships, doing randomized experiments can be difficult or impossible in some applications; hence, determining underlying causal relationships purely from observational data, i.e., causal discovery, has attracted more and more attention in many domains, such as earth science, biology, and healthcare. On the one hand, computational methods of causal discovery have been developed and improved significantly in the recent three decades. On the other hand, there are still many challenges in both practice and theory to further achieve real-world impacts. This thesis aims to introduce the typical methods and challenges of causal discovery and then elaborates on the contributions of the included papers that step forward to achieve more real-world impacts for causal discovery. It mainly covers four challenges: practical issues, understanding and generalizing the restrictive assumptions, the lack of benchmark data sets, and applications of causality in machine learning topics. Each included paper contributes to one of the challenges. In the first paper, regarding causal discovery in the presence of missing data as one of the practical issues, we theoretically study the influence of missing values on causal discovery methods and then correct the errors in their results. Under mild assumptions, our proposed method provides asymptotically correct results. In the second paper, we investigate the understanding of assumptions in a class of causal discovery methods. Such methods impose substantial constraints on functional classes and distributions of causal processes for determining causal relationships; however, the constraints are restrictive and there is a lack of good understanding. Therefore, we introduce a new dynamical-system view for understanding the methods and their constraints by connecting optimal transport and causal discovery., Målet för många vetenskapsområden är att hitta orsakssamband och förstå bakomliggande mekanismer. Som den gyllene standarden för att hitta orsakssamband kan slumpmässiga experiment vara svåra eller omöjliga i vissa tillämpningar; Därför har bestämning av underliggande orsakssamband enbart från observationsdata, d.v.s. kausal upptäckt, väckt mer och mer uppmärksamhet inom många områden, såsom geovetenskap, biologi och sjukvård. Å ena sidan har beräkningsmetoder för kausal upptäckt utvecklats och förbättrats avsevärt under de senaste tre decennierna. Å andra sidan finns det fortfarande många utmaningar kvar i både praktik och teori för att ytterligare uppnå verkliga effekter. Denna avhandling syftar till att introducera de typiska metoderna och utmaningarna för kausal upptäckt och sedan utveckla bidragen från de inkluderade artiklarna som tar kliv framåt för att uppnå fler verkliga effekter för kausal upptäckt. Den täcker huvudsakligen fyra utmaningar: praktiska frågor, förståelse och generalisering av de restriktiva antagandena, bristen på uppsättningar av referensdata och tillämpningar av kausalitet i maskininlärningsområden. Varje medföljande artikel bidrar till en av utmaningarna. I den första artikeln, angående kausal upptäckt i närvaro av saknade data som en av de praktiska frågorna, studerar vi teoretiskt saknade värdens inverkan på metoder för kausal upptäckt och korrigerar sedan felen i deras resultat. Under milda antaganden ger vår föreslagna metod korrekta resultat. I den andra artikeln undersöker vi förståelsen av antaganden i en klass av kausala upptäcktsmetoder. Sådana metoder lägger betydande begränsningar på funktionella klasser och fördelningar av kausala processer för att bestämma orsakssamband; dock är begränsningarna restriktiva och det saknas god förståelse. Därför introducerar vi en ny dynamisk systemvy för att förstå metoderna och deras begränsningar genom att koppla ihop optimal transport och kausal upptäckt. Dessutom tillhandahåller vi ett kau, QC 20221217

The goal of many sciences is to find causal relationships and understand underlying mechanisms. As the golden standard for finding causal relationships, doing randomized experiments can be difficult or impossible in some applications; hence, determining underlying causal relationships purely from observational data, i.e., causal discovery, has attracted more and more attention in many domains, such as earth science, biology, and healthcare. On the one hand, computational methods of causal discovery have been developed and improved significantly in the recent three decades. On the other hand, there are still many challenges in both practice and theory to further achieve real-world impacts. This thesis aims to introduce the typical methods and challenges of causal discovery and then elaborates on the contributions of the included papers that step forward to achieve more real-world impacts for causal discovery. It mainly covers four challenges: practical issues, understanding and generalizing the restrictive assumptions, the lack of benchmark data sets, and applications of causality in machine learning topics. Each included paper contributes to one of the challenges. In the first paper, regarding causal discovery in the presence of missing data as one of the practical issues, we theoretically study the influence of missing values on causal discovery methods and then correct the errors in their results. Under mild assumptions, our proposed method provides asymptotically correct results. In the second paper, we investigate the understanding of assumptions in a class of causal discovery methods. Such methods impose substantial constraints on functional classes and distributions of causal processes for determining causal relationships; however, the constraints are restrictive and there is a lack of good understanding. Therefore, we introduce a new dynamical-system view for understanding the methods and their constraints by connecting optimal transport and causal discovery., Målet för många vetenskapsområden är att hitta orsakssamband och förstå bakomliggande mekanismer. Som den gyllene standarden för att hitta orsakssamband kan slumpmässiga experiment vara svåra eller omöjliga i vissa tillämpningar; Därför har bestämning av underliggande orsakssamband enbart från observationsdata, d.v.s. kausal upptäckt, väckt mer och mer uppmärksamhet inom många områden, såsom geovetenskap, biologi och sjukvård. Å ena sidan har beräkningsmetoder för kausal upptäckt utvecklats och förbättrats avsevärt under de senaste tre decennierna. Å andra sidan finns det fortfarande många utmaningar kvar i både praktik och teori för att ytterligare uppnå verkliga effekter. Denna avhandling syftar till att introducera de typiska metoderna och utmaningarna för kausal upptäckt och sedan utveckla bidragen från de inkluderade artiklarna som tar kliv framåt för att uppnå fler verkliga effekter för kausal upptäckt. Den täcker huvudsakligen fyra utmaningar: praktiska frågor, förståelse och generalisering av de restriktiva antagandena, bristen på uppsättningar av referensdata och tillämpningar av kausalitet i maskininlärningsområden. Varje medföljande artikel bidrar till en av utmaningarna. I den första artikeln, angående kausal upptäckt i närvaro av saknade data som en av de praktiska frågorna, studerar vi teoretiskt saknade värdens inverkan på metoder för kausal upptäckt och korrigerar sedan felen i deras resultat. Under milda antaganden ger vår föreslagna metod korrekta resultat. I den andra artikeln undersöker vi förståelsen av antaganden i en klass av kausala upptäcktsmetoder. Sådana metoder lägger betydande begränsningar på funktionella klasser och fördelningar av kausala processer för att bestämma orsakssamband; dock är begränsningarna restriktiva och det saknas god förståelse. Därför introducerar vi en ny dynamisk systemvy för att förstå metoderna och deras begränsningar genom att koppla ihop optimal transport och kausal upptäckt. Dessutom tillhandahåller vi ett kau, QC 20221217

The goal of many sciences is to find causal relationships and understand underlying mechanisms. As the golden standard for finding causal relationships, doing randomized experiments can be difficult or impossible in some applications; hence, determining underlying causal relationships purely from observational data, i.e., causal discovery, has attracted more and more attention in many domains, such as earth science, biology, and healthcare. On the one hand, computational methods of causal discovery have been developed and improved significantly in the recent three decades. On the other hand, there are still many challenges in both practice and theory to further achieve real-world impacts. This thesis aims to introduce the typical methods and challenges of causal discovery and then elaborates on the contributions of the included papers that step forward to achieve more real-world impacts for causal discovery. It mainly covers four challenges: practical issues, understanding and generalizing the restrictive assumptions, the lack of benchmark data sets, and applications of causality in machine learning topics. Each included paper contributes to one of the challenges. In the first paper, regarding causal discovery in the presence of missing data as one of the practical issues, we theoretically study the influence of missing values on causal discovery methods and then correct the errors in their results. Under mild assumptions, our proposed method provides asymptotically correct results. In the second paper, we investigate the understanding of assumptions in a class of causal discovery methods. Such methods impose substantial constraints on functional classes and distributions of causal processes for determining causal relationships; however, the constraints are restrictive and there is a lack of good understanding. Therefore, we introduce a new dynamical-system view for understanding the methods and their constraints by connecting optimal transport and causal discovery., Målet för många vetenskapsområden är att hitta orsakssamband och förstå bakomliggande mekanismer. Som den gyllene standarden för att hitta orsakssamband kan slumpmässiga experiment vara svåra eller omöjliga i vissa tillämpningar; Därför har bestämning av underliggande orsakssamband enbart från observationsdata, d.v.s. kausal upptäckt, väckt mer och mer uppmärksamhet inom många områden, såsom geovetenskap, biologi och sjukvård. Å ena sidan har beräkningsmetoder för kausal upptäckt utvecklats och förbättrats avsevärt under de senaste tre decennierna. Å andra sidan finns det fortfarande många utmaningar kvar i både praktik och teori för att ytterligare uppnå verkliga effekter. Denna avhandling syftar till att introducera de typiska metoderna och utmaningarna för kausal upptäckt och sedan utveckla bidragen från de inkluderade artiklarna som tar kliv framåt för att uppnå fler verkliga effekter för kausal upptäckt. Den täcker huvudsakligen fyra utmaningar: praktiska frågor, förståelse och generalisering av de restriktiva antagandena, bristen på uppsättningar av referensdata och tillämpningar av kausalitet i maskininlärningsområden. Varje medföljande artikel bidrar till en av utmaningarna. I den första artikeln, angående kausal upptäckt i närvaro av saknade data som en av de praktiska frågorna, studerar vi teoretiskt saknade värdens inverkan på metoder för kausal upptäckt och korrigerar sedan felen i deras resultat. Under milda antaganden ger vår föreslagna metod korrekta resultat. I den andra artikeln undersöker vi förståelsen av antaganden i en klass av kausala upptäcktsmetoder. Sådana metoder lägger betydande begränsningar på funktionella klasser och fördelningar av kausala processer för att bestämma orsakssamband; dock är begränsningarna restriktiva och det saknas god förståelse. Därför introducerar vi en ny dynamisk systemvy för att förstå metoderna och deras begränsningar genom att koppla ihop optimal transport och kausal upptäckt. Dessutom tillhandahåller vi ett kau, QC 20221217

Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population's health worldwide, has been shown to deteriorate people's sleep quality. Numerous studies have been conducted to evaluate the impact of the COVID-19 pandemic on sleep efficiency, investigating their relationships using correlation-based methods. These methods merely rely on learning spurious correlation rather than the causal relations among variables. Furthermore, they fail to pinpoint potential sources of bias and mediators and envision counterfactual scenarios, leading to a poor estimation. In this paper, we develop a Causal Machine Learning method, which encompasses causal discovery and causal inference components, to extract the causal relations between the COVID-19 pandemic (treatment variable) and sleep quality (outcome) and estimate the causal treatment effect, respectively. We conducted a wearable-based health monitoring study to collect data, including sleep quality, physical activity, and Heart Rate Variability (HRV) from college students before and after the COVID-19 lockdown in March 2020. Our causal discovery component generates a causal graph and pinpoints mediators in the causal model. We incorporate the strongly contributing mediators (i.e., HRV and physical activity) into our causal inference component to estimate the robust, accurate, and explainable causal effect of the pandemic on sleep quality. Finally, we validate our estimation via three refutation analysis techniques. Our experimental results indicate that the pandemic exacerbates college students' sleep scores by 8%. Our validation results show significant p-values confirming our estimation.

In the era of rapid growth and transformation driven by artificial intelligence across various sectors, which is catalyzing the fourth industrial revolution, this research is directed toward harnessing its potential to enhance the efficiency of decision-making processes within organizations. When constructing machine learning-based decision models, a fundamental step involves the conversion of domain knowledge into causal-effect relationships that are represented in causal graphs. This process is also notably advantageous for constructing explanation models. We present a method for generating causal graphs that integrates the strengths of Large Language Models (LLMs) with traditional causal theory algorithms. Our method seeks to bridge the gap between AI’s theoretical potential and practical applications. In contrast to recent related works that seek to exclude the involvement of domain experts, our method places them at the forefront of the process. We present a novel pipeline that streamlines and enhances domain-expert validation by providing robust causal graph proposals. These proposals are enriched with transparent reports that blend foundational causal theory reasoning with explanations from LLMs.

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graphin which the properties of the reduced graph are to be induced from the properties of the largeroriginal graph. This paper introduces both a new method for reducing chain graphs to simplerdirected acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure forstructure learning of this new type of graph from correlational data of a Gaussian graphical model.Adefinitionfor PCGs is given, directly followed by the reduction method. The structure learningprocedure is a two-step approach:first,the correlation matrix is used to cluster the variables; andthen, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm.The results of simulations are provided to illustrate the theoretical proposal, which demonstrateinitial evidence for the validity of our procedure to recover the structure of power chain graphs.The paper ends with a discussion regarding suggestions for future studies as well as some practicalimplications

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graphin which the properties of the reduced graph are to be induced from the properties of the largeroriginal graph. This paper introduces both a new method for reducing chain graphs to simplerdirected acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure forstructure learning of this new type of graph from correlational data of a Gaussian graphical model.Adefinitionfor PCGs is given, directly followed by the reduction method. The structure learningprocedure is a two-step approach:first,the correlation matrix is used to cluster the variables; andthen, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm.The results of simulations are provided to illustrate the theoretical proposal, which demonstrateinitial evidence for the validity of our procedure to recover the structure of power chain graphs.The paper ends with a discussion regarding suggestions for future studies as well as some practicalimplications

Designing agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone., Aerospace Transport & Operations

To operate in open world environments a symbolic Artificial Intelligence (AI) to be able to adapt and incorporate new objects and relations in its Knowledge Base (KB). Symbolic AI use the objects and relations in their KB to navigate the world and create plans. These KB are filled with knowledge in advance, so they can not add new knowledge when encountering novel situations. This thesis presents Heuristics-Based Causal Discovery (HBCD), a method that identifies and labels causal relations and transforms those causal relations into logic statements, which can be inserted into a KB autonomously. HBCD can operate in a partially-observable environment by performing actions and observing the effects of its actions. The actions are chosen by heuristics, which are modelled after human strategies for causal discovery. The discovered causal relations are tested for the properties necessity and sufficiency. These properties provide information on the completeness of the result, whether there are any missing causal relations. HBCD uses this information to adjust its search space by moving variables in and out of it during the search. If there are no more missing causal relations HBCD stops the search. The method was tested in two simulated environments and the results are promising.

Causal discovery is the problem of learning causal relationships between variables from a set of data. One interesting area of use for causal discovery is the health care domain, where application could help facilitate a better understanding of disease and treatment mechanisms. The health care domain has recently undergone a major digitization, making available a large amount of data for use in learning algorithms, available in formats such as medical images or electronic health records. This thesis aims to explore the application of causal discovery on electronic health record data. We provide an overview of the field of causal discovery and identify 3 contemporary methods for causal discovery on time-series data which we apply on a preprocessed version of the MIMIC-IV data set. Each causal discovery method is run on time-series comprising of electronic health record data related to hospital stays for patients with sepsis. We provide an empiric report of the overlap between the learned graphs from different hospital stays as a heuristic evaluation measure. We find that it is possible to identify common themes in the learned graphs between different causal discovery methods, indicating potential practical value of causal discovery on electronic health record data. We also identify important considerations for future application and evaluation, such as incorporating extensive domain knowledge, and provide suggestions for future work., Kausal upptäckt är problemet med att lära sig orsakssamband mellan variabler från en uppsättning data. Ett intressant användningsområde för kausal upptäckt är hälso- och sjukvårdsdomänen, där tillämpning kan bidra till en bättre förståelse av sjukdomar och behandlingsmekanismer. Sjukvårdsdomänen har nyligen genomgått en stor digitalisering vilket gör en stor mängd data tillgänglig för användning i inlärningsalgoritmer, tillgänglig i format som medicinska bilder eller elektroniska patientjournaler. Denna avhandling syftar till att utforska tillämpningen av kausal upptäckt på elektroniska patientjournaler. Vi ger en översikt över området för kausal upptäckt och identifierar 3 samtida metoder för kausal upptäckt på tidsseriedata som vi tillämpar på en förbearbetad version av MIMIC-IV-datauppsättningen. Varje identifierad metod för kausal upptäckt körs på tidsserier som består av elektroniska patientjournaler relaterade till sjukhusvistelser för patienter med sepsis. Vi tillhandahåller en empirisk rapport över överlappningen mellan de inlärda graferna från olika sjukhusvistelser som ett heuristiskt utvärderingsmått. Vi finner att det är möjligt att identifiera gemensamma teman i de inlärda graferna mellan olika kausala upptäcktsmetoder, vilket indikerar potentiellt praktiskt värde av kausal upptäckt på elektroniska patientjournaler. Vi identifierar också viktiga överväganden för framtida tillämpning och utvärdering, såsom att integrera omfattande domänkunskap, och ger förslag för framtida arbete.

In “Flagpoles anyone? Causal and explanatory asymmetries”, James Woodward supplements his celebrated interventionist account of causation and explanation with a set of new ideas about causal and explanatory asymmetries, which he extracts from some cutting-edge methods for causal discovery from observational data. Among other things, Woodward draws interesting connections between observational causal discovery and interventionist themes that are inspired in the first place by experimental causal discovery, alluding to a sort of unity between observational and experimental causal discovery. In this paper, I make explicit what I take to be the implicated unity. Like experimental causal discovery, observational causal discovery also relies on interventions (or exogenous variations, to be more accurate), albeit interventions that are not carried out by investigators and hence need to be detected as part of the inference. The observational patterns appealed to in observational causal discovery are not only surrogates for would-be interventions, as Woodward sometimes puts it; they also serve to mark relevant interventions that actually happen in the data generating process., En “Flagpoles anyone? Causal and explanatory asymmetries”, James Woodward complementa su celebrada teoría intervencionista de la causación y la explicación con nuevas ideas sobre asimetrías causales y explicativas, extraídas de recientes métodos de descubrimiento causal a partir de datos observacionales. Entre otras cosas, Woodward establece interesantes conexiones entre el descubrimiento causal observacional e ideas intervencionistas inspiradas inicialmente en el descubrimiento causal experimental, aludiendo a cierta unidad entre el descubrimiento causal observacional y experimental. Al igual que el descubrimiento causal experimental, el descubrimiento causal observacional también se apoya en intervenciones (o variaciones exógenas, para ser más precisos), aunque sean intervenciones que no son realizadas por investigadores y por tanto tienen que ser detectadas como parte de la inferencia. Los patrones observacionales a los que se apela en el descubrimiento causal observacional no son los sustitutos de posibles intervenciones, como Woodward algunas veces sugiere; también sirven para marcar intervenciones relevantes que de hecho tienen lugar en el proceso de generación de datos.

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graphin which the properties of the reduced graph are to be induced from the properties of the largeroriginal graph. This paper introduces both a new method for reducing chain graphs to simplerdirected acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure forstructure learning of this new type of graph from correlational data of a Gaussian graphical model.Adefinitionfor PCGs is given, directly followed by the reduction method. The structure learningprocedure is a two-step approach:first,the correlation matrix is used to cluster the variables; andthen, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm.The results of simulations are provided to illustrate the theoretical proposal, which demonstrateinitial evidence for the validity of our procedure to recover the structure of power chain graphs.The paper ends with a discussion regarding suggestions for future studies as well as some practicalimplications

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graphin which the properties of the reduced graph are to be induced from the properties of the largeroriginal graph. This paper introduces both a new method for reducing chain graphs to simplerdirected acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure forstructure learning of this new type of graph from correlational data of a Gaussian graphical model.Adefinitionfor PCGs is given, directly followed by the reduction method. The structure learningprocedure is a two-step approach:first,the correlation matrix is used to cluster the variables; andthen, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm.The results of simulations are provided to illustrate the theoretical proposal, which demonstrateinitial evidence for the validity of our procedure to recover the structure of power chain graphs.The paper ends with a discussion regarding suggestions for future studies as well as some practicalimplications

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.

Causal discovery is the problem of learning causal relationships between variables from a set of data. One interesting area of use for causal discovery is the health care domain, where application could help facilitate a better understanding of disease and treatment mechanisms. The health care domain has recently undergone a major digitization, making available a large amount of data for use in learning algorithms, available in formats such as medical images or electronic health records. This thesis aims to explore the application of causal discovery on electronic health record data. We provide an overview of the field of causal discovery and identify 3 contemporary methods for causal discovery on time-series data which we apply on a preprocessed version of the MIMIC-IV data set. Each causal discovery method is run on time-series comprising of electronic health record data related to hospital stays for patients with sepsis. We provide an empiric report of the overlap between the learned graphs from different hospital stays as a heuristic evaluation measure. We find that it is possible to identify common themes in the learned graphs between different causal discovery methods, indicating potential practical value of causal discovery on electronic health record data. We also identify important considerations for future application and evaluation, such as incorporating extensive domain knowledge, and provide suggestions for future work., Kausal upptäckt är problemet med att lära sig orsakssamband mellan variabler från en uppsättning data. Ett intressant användningsområde för kausal upptäckt är hälso- och sjukvårdsdomänen, där tillämpning kan bidra till en bättre förståelse av sjukdomar och behandlingsmekanismer. Sjukvårdsdomänen har nyligen genomgått en stor digitalisering vilket gör en stor mängd data tillgänglig för användning i inlärningsalgoritmer, tillgänglig i format som medicinska bilder eller elektroniska patientjournaler. Denna avhandling syftar till att utforska tillämpningen av kausal upptäckt på elektroniska patientjournaler. Vi ger en översikt över området för kausal upptäckt och identifierar 3 samtida metoder för kausal upptäckt på tidsseriedata som vi tillämpar på en förbearbetad version av MIMIC-IV-datauppsättningen. Varje identifierad metod för kausal upptäckt körs på tidsserier som består av elektroniska patientjournaler relaterade till sjukhusvistelser för patienter med sepsis. Vi tillhandahåller en empirisk rapport över överlappningen mellan de inlärda graferna från olika sjukhusvistelser som ett heuristiskt utvärderingsmått. Vi finner att det är möjligt att identifiera gemensamma teman i de inlärda graferna mellan olika kausala upptäcktsmetoder, vilket indikerar potentiellt praktiskt värde av kausal upptäckt på elektroniska patientjournaler. Vi identifierar också viktiga överväganden för framtida tillämpning och utvärdering, såsom att integrera omfattande domänkunskap, och ger förslag för framtida arbete.

Causal skeleton learning aims to identify the undirected graph of the underlying causal Bayesian network (BN) from observational data. It plays a pivotal role in causal discovery and many other downstream applications. The methods for causal skeleton learning fall into three primary categories: constraint-based, score-based, and gradient-based methods. This paper, for the first time, advocates for learning a causal skeleton in a supervision-based setting, where the algorithm learns from additional datasets associated with the ground-truth BNs (complementary to input observational data). Concretizing a supervision-based method is non-trivial due to the high complexity of the problem itself, and the potential "domain shift"between training data (i.e., additional datasets associated with ground-truth BNs) and test data (i.e., observational data) in the supervision-based setting. First, it is well-known that skeleton learning suffers worst-case exponential complexity. Second, conventional supervised learning assumes an independent and identical distribution (i.i.d.) on test data, which is not easily attainable due to the divergent underlying causal mechanisms between training and test data. Our proposed framework, ML4S, adopts order-based cascade classifiers and pruning strategies that can withstand high computational overhead without sacrificing accuracy. To address the "domain shift"challenge, we generate training data from vicinal graphs w.r.t. the target BN. The associated datasets of vicinal graphs share similar joint distributions with the observational data. We evaluate ML4S on a variety of datasets and observe that it remarkably outperforms the state of the arts, demonstrating the great potential of the supervision-based skeleton learning paradigm. © 2022 ACM.

Causal inference is widely used in clinical research, economic analysis, and other fields. As is the case with many statistical data, the findings of causal discovery (i.e., causal graph) might leak demographic information of participants. For example, a causal link between one genome and a rare disease can reveal the participation of a minority patient in genome-wide association studies. To date, differential privacy has served as the de facto foundation for guaranteeing the privacy of causal discovery algorithms. However, existing approaches to protecting causal discovery from privacy leakage rely heavily on private conditional independence tests, which generate a considerable amount of noise and are thus prone to inaccuracy. As a result of their limited accuracy and scalability, they are insufficient for non-trivial datasets (e.g., those with more than ten variables). In this paper, we advocate a novel focus on enforcing privacy for causal discovery algorithms based on functional causal models. First, we propose NoLeaks, a differentially private causal discovery algorithm, which manifests both high accuracy and efficiency compared with prior works. Second, we design a quasi-Newton numerical optimization algorithm for solving NoLeaks in a highly efficient way. Third, we evaluate NoLeaks using both public benchmarks and synthetic data. We observe that NoLeaks achieves comparable performance or even surpasses the state-of-the-art (non-private) approaches. We also find encouraging results that NoLeaks can smoothly scale to large datasets, on which existing works would fail. Through a case study and a downstream application, we observe encouraging results on the versatile usages of NoLeaks. © 2005-2012 IEEE.

Designing agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Air Transport & Operations

To operate in open world environments a symbolic Artificial Intelligence (AI) to be able to adapt and incorporate new objects and relations in its Knowledge Base (KB). Symbolic AI use the objects and relations in their KB to navigate the world and create plans. These KB are filled with knowledge in advance, so they can not add new knowledge when encountering novel situations. This thesis presents Heuristics-Based Causal Discovery (HBCD), a method that identifies and labels causal relations and transforms those causal relations into logic statements, which can be inserted into a KB autonomously. HBCD can operate in a partially-observable environment by performing actions and observing the effects of its actions. The actions are chosen by heuristics, which are modelled after human strategies for causal discovery. The discovered causal relations are tested for the properties necessity and sufficiency. These properties provide information on the completeness of the result, whether there are any missing causal relations. HBCD uses this information to adjust its search space by moving variables in and out of it during the search. If there are no more missing causal relations HBCD stops the search. The method was tested in two simulated environments and the results are promising., Mechanical Engineering

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

Despite having a philosophical grounding from empiricism that spans some centuries, the algorithmization of causal discovery started only a few decades ago. This formalization of studying causal relationships relies on connections between graphs and probability distributions. In this setting, the task of causal discovery is to recover the graph that best describes the causal structure based on the available data. A particular class of causal discovery algorithms, called constraint-based methods rely on Directed Acyclic Graphs (DAGs) as an encoding of Conditional Independence (CI) relations that carry some level of causal information. However, a CI relation such as X and Y being independent conditioned on Z assumes the independence holds for all possible values Z can take, which can tend to be unrealistic in practice where causal relations are often context-specific. In this thesis we aim to develop constraint-based algorithms to learn causal structure from Context-Specific Independence (CSI) relations within the discrete setting, where the independence relations are of the form X and Y being independent given Z and C = a for some a. This is done by using Context-Specific trees, or CStrees for short, which can encode CSI relations., Trots att ha en filosofisk grund från empirism som sträcker sig över några århundraden, algoritm isering av kausal upptäckt startade för bara några decennier sedan. Denna formalisering av att studera orsakssamband beror på samband mellan grafer och sannolikhetsfördelningar. I den här inställningen är kausal upptäckt att återställa grafen som bäst beskriver kausal strukturen baserat på tillgängliga data. En särskild klass av kausala upptäckts algoritmer, så kallade begränsnings baserade metoder, är beroende av Directed Acyclic Graphs (DAG) som en kodning av förhållanden med villkorlig självständighet (CI) som bär någon nivå av kausal information. En CI-relation som X och Y är oberoende förutsatt att Z förutsätter att oberoende gäller för alla möjliga värden som Z kan ta, vilket kan vara orealistiskt i praktiken där orsakssamband ofta är kontextspecifika. I denna avhandling strävar vi efter att utveckla begränsnings baserade algoritmer för att lära kausal struktur från Contex-Specific Independence (CSI) -relationer inom den diskreta miljön, där självständighet relationerna har formen X och Y är oberoende med tanke på Z och C = a för vissa a. Detta görs genom att använda sammanhang specifika träd, eller kortfattat CStrees, som kan koda CSI-relationer.

Performing causal reasoning in a scene is an inherent mechanism in human cognition; however, the majority of approaches in the causality literature aiming for this task still consider constrained scenarios, such as simple physical systems or stationary time-series data. In this work we aim for causal discovery in videos concerning realistic scenarios. We gather motivation for causal discovery by acknowledging this task to be core at human cognition. Moreover, we interpret the scene as a composition of time-series that interact along the sequence and aim for modeling the non-stationary behaviors in a scene. We propose State-dependent Causal Inference (SDCI) for causal discovery in conditional stationary time-series data. We formulate our problem of causal analysis by considering that the stationarity of the time-series is conditioned on a categorical variable, which we call state. Results show that the probabilistic implementation proposed achieves outstanding results in identifying causal relations on simulated data. When considering the state being independent from the dynamics, our method maintains decent accuracy levels of edge-type identification achieving 74.87% test accuracy when considering a total of 8 states. Furthermore, our method correctly handles regimes where the state variable undergoes complex transitions and is dependent on the dynamics of the scene, achieving 79.21% accuracy in identifying the causal interactions. We consider this work to be an important contribution towards causal discovery in videos., Att utföra kausala resonemang i en scen är en medfödd mekanism i mänsklig kognition; dock betraktar fortfarande majoriteten av tillvägagångssätt i kausalitetslitteraturen, som syftar till denna uppgift, begränsade scenarier såsom enkla fysiska system eller stationära tidsseriedata. I detta arbete strävar vi efter kausal upptäckt i videor om realistiska scenarier. Vi samlar motivation för kausal upptäckt genom att erkänna att denna uppgift är kärnan i mänsklig kognition. Dessutom tolkar vi scenen som en komposition av tidsserier som interagerar längs sekvensen och syftar till att modellera det icke-stationära beteendet i en scen. Vi föreslår Tillståndsberoende kausal inferens (SDCI) för kausal upptäckt i villkorlig stationär tidsseriedata. Vi formulerar vårt problem med kausalanalys genom att anse att tidsseriens stationäritet är villkorad av en kategorisk variabel, som vi kallar tillstånd. Resultaten visar att det föreslagna probabilistiska genomförandet uppnår enastående resultat vid identifiering av orsakssambandet på simulerade data. När man överväger att tillståndet är oberoende av dynamiken, upprätthåller vår metod anständiga noggrannhetsnivåer av kanttypsidentifiering som uppnår 74, 87% testnoggrannhet när man överväger totalt 8 tillstånd. Dessutom hanterar vår metod korrekt regimer där tillståndsvariabeln genomgår komplexa övergångar och är beroende av dynamiken på scenen och uppnår 79, 21% noggrannhet för att identifiera kausala interaktioner. Vi anser att detta arbete är ett viktigt bidrag till kausal upptäckt i videor.

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

Despite having a philosophical grounding from empiricism that spans some centuries, the algorithmization of causal discovery started only a few decades ago. This formalization of studying causal relationships relies on connections between graphs and probability distributions. In this setting, the task of causal discovery is to recover the graph that best describes the causal structure based on the available data. A particular class of causal discovery algorithms, called constraint-based methods rely on Directed Acyclic Graphs (DAGs) as an encoding of Conditional Independence (CI) relations that carry some level of causal information. However, a CI relation such as X and Y being independent conditioned on Z assumes the independence holds for all possible values Z can take, which can tend to be unrealistic in practice where causal relations are often context-specific. In this thesis we aim to develop constraint-based algorithms to learn causal structure from Context-Specific Independence (CSI) relations within the discrete setting, where the independence relations are of the form X and Y being independent given Z and C = a for some a. This is done by using Context-Specific trees, or CStrees for short, which can encode CSI relations., Trots att ha en filosofisk grund från empirism som sträcker sig över några århundraden, algoritm isering av kausal upptäckt startade för bara några decennier sedan. Denna formalisering av att studera orsakssamband beror på samband mellan grafer och sannolikhetsfördelningar. I den här inställningen är kausal upptäckt att återställa grafen som bäst beskriver kausal strukturen baserat på tillgängliga data. En särskild klass av kausala upptäckts algoritmer, så kallade begränsnings baserade metoder, är beroende av Directed Acyclic Graphs (DAG) som en kodning av förhållanden med villkorlig självständighet (CI) som bär någon nivå av kausal information. En CI-relation som X och Y är oberoende förutsatt att Z förutsätter att oberoende gäller för alla möjliga värden som Z kan ta, vilket kan vara orealistiskt i praktiken där orsakssamband ofta är kontextspecifika. I denna avhandling strävar vi efter att utveckla begränsnings baserade algoritmer för att lära kausal struktur från Contex-Specific Independence (CSI) -relationer inom den diskreta miljön, där självständighet relationerna har formen X och Y är oberoende med tanke på Z och C = a för vissa a. Detta görs genom att använda sammanhang specifika träd, eller kortfattat CStrees, som kan koda CSI-relationer.

Performing causal reasoning in a scene is an inherent mechanism in human cognition; however, the majority of approaches in the causality literature aiming for this task still consider constrained scenarios, such as simple physical systems or stationary time-series data. In this work we aim for causal discovery in videos concerning realistic scenarios. We gather motivation for causal discovery by acknowledging this task to be core at human cognition. Moreover, we interpret the scene as a composition of time-series that interact along the sequence and aim for modeling the non-stationary behaviors in a scene. We propose State-dependent Causal Inference (SDCI) for causal discovery in conditional stationary time-series data. We formulate our problem of causal analysis by considering that the stationarity of the time-series is conditioned on a categorical variable, which we call state. Results show that the probabilistic implementation proposed achieves outstanding results in identifying causal relations on simulated data. When considering the state being independent from the dynamics, our method maintains decent accuracy levels of edge-type identification achieving 74.87% test accuracy when considering a total of 8 states. Furthermore, our method correctly handles regimes where the state variable undergoes complex transitions and is dependent on the dynamics of the scene, achieving 79.21% accuracy in identifying the causal interactions. We consider this work to be an important contribution towards causal discovery in videos., Att utföra kausala resonemang i en scen är en medfödd mekanism i mänsklig kognition; dock betraktar fortfarande majoriteten av tillvägagångssätt i kausalitetslitteraturen, som syftar till denna uppgift, begränsade scenarier såsom enkla fysiska system eller stationära tidsseriedata. I detta arbete strävar vi efter kausal upptäckt i videor om realistiska scenarier. Vi samlar motivation för kausal upptäckt genom att erkänna att denna uppgift är kärnan i mänsklig kognition. Dessutom tolkar vi scenen som en komposition av tidsserier som interagerar längs sekvensen och syftar till att modellera det icke-stationära beteendet i en scen. Vi föreslår Tillståndsberoende kausal inferens (SDCI) för kausal upptäckt i villkorlig stationär tidsseriedata. Vi formulerar vårt problem med kausalanalys genom att anse att tidsseriens stationäritet är villkorad av en kategorisk variabel, som vi kallar tillstånd. Resultaten visar att det föreslagna probabilistiska genomförandet uppnår enastående resultat vid identifiering av orsakssambandet på simulerade data. När man överväger att tillståndet är oberoende av dynamiken, upprätthåller vår metod anständiga noggrannhetsnivåer av kanttypsidentifiering som uppnår 74, 87% testnoggrannhet när man överväger totalt 8 tillstånd. Dessutom hanterar vår metod korrekt regimer där tillståndsvariabeln genomgår komplexa övergångar och är beroende av dynamiken på scenen och uppnår 79, 21% noggrannhet för att identifiera kausala interaktioner. Vi anser att detta arbete är ett viktigt bidrag till kausal upptäckt i videor.

Many problems in the study of dynamical systems—including identification of effective order, detection of nonlinearity or chaos, and change detection—can be reframed in terms of assessing the similarity between dynamical systems or between a given dynamical system and a reference. We introduce a general metric of dynamical similarity that is well posed for both stochastic and deterministic systems and is informative of the aforementioned dynamical features even when only partial information about the system is available. We describe methods for estimating this metric in a range of scenarios that differ in respect to contol over the systems under study, the deterministic or stochastic nature of the underlying dynamics, and whether or not a fully informative set of variables is available. Through numerical simulation, we demonstrate the sensitivity of the proposed metric to a range of dynamical properties, its utility in mapping the dynamical properties of parameter space for a given model, and its power for detecting structural changes through time series data.

[ES] GfK owns the world’s largest retail panel within the tech and durable good industries. The panel consists of weekly Point of Sales (PoS) data, such as price and sales units data at store level. From PoS data and other data, GfK derives insights and indicators to generate recommendations with regards to e.g. pricing, distribution or assortment optimization of tech and durable good products. By combining PoS data and business domain knowledge, we show how causal discovery can be done by applying the method of invariant causal prediction (ICP). Causal discovery, in essence, means to learn the actual cause and effect relations between the involved variables from data. After finding such a causal structure, one can try to further specify the function classes between those identified cause-effect pairs. Such a model could then be used to predict under intervention (predict when the underlying data generating mechanism changes) and to optimize and calculate counterfactual effects, given current and past data. In our development, we combine recent achievements in causal discovery research with PoS data structure and business domain knowledge (in the form of business rules). The key delivery of this presentation is to show fundamental differences between a causal model and a machine learning model. We further explain the advantages of combining a causal model with a machine learning model and why causal information is key to provide explainable prescriptive analytics. Furthermore, we demonstrate how to apply ICP (for sequential data) to context-specific PoS data to achieve improved models for sales unit predictions. As a result, we obtain a model for sales units that is on the one hand derived from observed data and on the other hand driven by business knowledge. Such a refined prediction model could then be used to stabilize and support other machine learning models that can be used for generating prescriptive analytics.

Directed acyclic graph (DAG) are widely used for modeling all kinds of relations and processes. Learning DAG structure from observational data is a challenging problem. In this dissertation, we develop novel methods for learning causal DAGs. First we work on learning the DAG structure with predefined outcomes by a Bayesian hierarchical model, where the outcomes are ``mixed'' in the sense that some may be continuous, some binary and others may be counts. We concern with multivariate point-referenced data, where each location yields measurements on multiple variables and build a joint model by hierarchically building conditional distributions with different spatial processes embedded in each conditional distribution. Comparisons in simulation study and data analysis across different spatial models show that the performance is greatly improved by incorporating spatial processes. The second problem we try to solve is the causal relation determination in the bivariate case, i.e. to determine the direction between X and Y. We restrict our attention to non-invertible causal relations between variables and develop a novel testing method for deciding the causal direction. We propose a bivariate nonlinear SEM, where the function f is non-invertible and identify the causal direction by testing for the non-invertibility of the function f. Piecewise linear function with two pieces can capture the change in functional relationships and is used for approximating the non-invertible function f. Causal DAGs are usually not identifiable from observational data and most existing methods only estimate a DAG up to its equivalence class. For the situation with more than two variables, instead of applying the bivariate non-invertible causal model to all of the given variables directly, we avoid the exhaustive search over all possible causal structures by combining current linear structure learning algorithms with our non-invertible causal learning algorithm. In our implementation, an initi