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Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.

Instrumental variables is a popular approach for causal inference in education when randomization of treatment is not feasible. Using a first-year college program as a running example, this article reviews the five assumptions that must be met to successfully use instrumental variables to estimate a causal effect with observational data: SUTVA, as-if random assignment, exclusion restriction, nonzero average causal effect of instrument on treatment, and monotonicity, and concludes with recommendations for researchers.

Background: Lipid metabolism plays an essential role in nervous system development. Cholesterol deficiency leads to a variety of neurodevelopmental disorders, such as autism spectrum disorder and fragile X syndrome. There have been a lot of efforts to search for biological markers associated with and causal to ADHD, among which lipid is one possible etiological factor that is quite widely studied. We aimed to evaluate the causal relationship between lipids traits, lipid-lowering drugs, and attention deficit hyperactivity disorder (ADHD) outcomes using Mendelian randomization (MR) studies. Methods: We used summary data from genome-wide association studies to explore the causal relationships between circulating lipid related traits and ADHD. Then, quantitative trait loci for the expression of lipid-lowering drug target genes and genetic variants associated with lipid traits were extracted. Summary-data-based MR and inverse-variance-weighted MR (IVWMR) were used to investigate the correlation between the expression of these drug-target genes and ADHD. Results: After rigorous screening, 939 instrumental variables were finally included for univariable mendelian randomization analysis. However, there is no correlation between lipid profile and ADHD risk. Drug target analysis by IVW-MR method observed that APOB-mediated low-density lipoprotein cholesterol was associated with lower ADHD risk (odds ratio [OR] = 0.90, 95% confidence interval [CI] [0.84, 0.97]; p = .007), whereas "LPL"-mediated triglycerides levels were associated with a higher risk of ADHD (OR = 1.13, 95% CI [1.06, 1.21]; p < .001). Conclusion: Our results suggest that "APOB" gene and "LPL" gene may be candidate drug target genes for the treatment of ADHD.

As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their inception, propensity scores have made an important contribution to the improvement in estimation of causal impacts, particularly in the absence of randomization. When certain core assumptions hold, propensity score-based methods allow for bias-reduced estimation of average treatment effects. In addition to their important role in causal studies, propensity scores have also been integral in improving generalizations from causal studies, specifically when study samples are not randomly selected from a target population of inference. The current study provides an overview of propensity scores, a discussion of the assumptions needed to ensure their validity, and an illustration of the methods both for causal inference and generalization. We highlight the importance of propensity score methods and discuss current applications and directions for ongoing and future research.

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed.

Research in the social and behavioral sciences relies on a wide range of experimental and quasi-experimental designs to estimate the causal effects of specific programs, policies, and events. In this paper we highlight measurement issues relevant to evaluating the validity of causal estimation and generalization. These issues impact all four categories of threats to validity previously delineated by Shadish et al. (Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin, Boston, 2002): internal, external, statistical conclusion, and construct validity. We use the context of estimating the effect of the COVID-19 pandemic on student learning in the U.S. to illustrate the important role of measurement in causal inference. We provide background related to the meaning of measurement, and focus attention on the evidence and argumentation necessary to evaluate the validity and reliability of the different types of measures used in statistical models for causal inference. We conclude with recommendations for researchers estimating and generalizing causal effects: provide clear statements for construct interpretations, seek to rule out potential sources of construct-irrelevant variance, quantify and adjust for measurement error, and consider the extent to which interpretations of practical significance are consistent with scale properties of outcome measures.

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental designs: regression discontinuity design, difference-in-differences analysis, interrupted time series design, instrumental variable analysis, and propensity score analysis with examples in education research.

Causal mediation analysis has gained increasing attention in recent years. This article guides empirical researchers through the concepts and challenges of causal mediation analysis. I first clarify the difference between traditional and causal mediation analysis and highlight the importance of adjusting for the treatment-by-mediator interaction and confounders of the treatment-mediator, treatment-outcome, and mediator-outcome relationships. I then introduce the definition of causal mediation effects under the potential outcomes framework and different methods for the identification and estimation of the effects. After that, I highlight the importance of conducting a sensitivity analysis to assess the sensitivity of analysis results to potential unmeasured confounding. I also list various statistical software that can conduct causal mediation analysis and sensitivity analysis and provide suggestions for writing a causal mediation analysis paper. Finally, I briefly introduce some extensions that I made with my colleagues, including power analysis, multisite causal mediation analysis, causal moderated mediation analysis, and relaxing the assumption of no post-treatment confounding.

Causal inference is a central topic in education research, although oftentimes it relies on observational studies, which makes causal identification methodologically challenging. This manuscript introduces causal graphs as a powerful language for elucidating causal theories and an effective tool for causal identification analysis. It discusses graphical criteria for causal identification, which provide principled approaches for removing bias and assessing causal identification given a causal theory. Through illustrative examples, this manuscript demonstrates the application of causal graphs and adjustment criterion for covariate selection in the context of education research, exemplifying their key advantages particularly in scenarios where randomized experiments are impractical. This manuscript aims to acquaint researchers with causal graphs as an effective tool for causal inference, thereby facilitating theory-based causal inquiries in applied education research.

Regression discontinuity (RD) designs have gained significant popularity as a quasi-experimental device for evaluating education programs and policies. In this paper, we present a comprehensive review of RD designs, focusing on the continuity-based framework, the most widely adopted RD framework. We first review the fundamental aspects of RD designs, drawing on potential outcomes and causal graphs. We then discuss the validity threats in RD designs, including manipulation, discreteness of the running variable, statistical power, and generalizability. Additionally, we provide an overview of the existing extensions to RD designs. To exemplify the application of RD methods, we analyze the effect of New Jersey's pre-kindergarten program on children's vocabulary test scores, using an educational dataset. Finally, we offer practical guidelines in the conclusion to promote the appropriate use of RD methods in educational research.

Given decision-makers often prioritize causal research that identifies the impact of treatments on the people they serve, a key question in education research is, "Does it work?". Today, however, researchers are paying increasing attention to successive questions that are equally important from a practical standpoint--not only does it work, but for whom and under what circumstances? Invalid conclusions to any of these questions can result in the adoption of ineffective educational practices. This article discusses the enduring legacy of Shadish, Cook, and Campbell's validity typology, and its associated threats to validity, for improving the validity of inferences in education research. The validity typology provides a system for classifying and improving inferences related to four validity types, including ensuring a causal relationship between a treatment and outcome (internal validity) that is precisely estimated (statistical validity), well understood (construct validity), and generalizes to the necessary circumstances (external validity). Here, we provide an overview of these four validity types and discuss proactive approaches to addressing them. We conclude by discussing how the validity typology framework may help researchers understand and address contemporary critiques of quantitative causal research.

Purpose: Aging increases risk for hearing loss, cognitive decline, and social isolation; however, the nature of their interconnection remains unclear. This study examined the interplay between age-related hearing loss, cognitive decline, and social isolation in adults by testing the ability to understand speech in background noise, a challenge frequently reported by many older adults. Method: We analyzed data collected from 128 adults (20-79 years of age, M[subscript age] = 51 years) recruited as part of the Aging Brain Cohort at the University of South Carolina repository. The participants underwent testing for hearing, cognition, and social interaction, which included pure-tone audiometry, a words-in-noise (WIN) test, a hearing questionnaire (Speech, Spatial and Qualities of Hearing Scale [SSQ12]), a social questionnaire (Patient-Reported Outcomes Measurement Information System-57 Social), and the Montreal Cognitive Assessment. We used a single pure-tone average (PTA) threshold value and a single WIN threshold value for each participant because there were no differences on average between the left and right ears. Results: Poorer hearing was significantly associated with cognitive decline, through both PTA and WIN thresholds, with a stronger association observed for WIN threshold. Adults with poorer hearing also exhibited greater social isolation, as evidenced by their WIN threshold and SSQ12 score, although not through PTA. This connection was more pronounced with the WIN threshold than with the SSQ12 score. Cognition was not related to social isolation, suggesting that social isolation is affected more by the ability to understand words in noise than by cognition in a nondemented population. Conclusions: Understanding speech in challenging auditory environments rather than mere threshold detection is strongly linked to social isolation and cognitive decline. Thus, inclusion of a word-recognition-in-noise test and a social isolation survey in clinical settings is warranted.

The impact of a major historical event on child and youth development has been of great interest in the study of the life course. This study is focused on assessing the causal effect of the Great Recession on youth disconnection from school and work. Building on the insights offered by the age-period-cohort research, econometric methods, and developmental psychology, we innovatively develop a causal inference strategy that takes advantage of the multiple successive birth cohorts in the National Longitudinal Study of Youth 1997. The causal effect of the Great Recession is defined in terms of counterfactual developmental trajectories and can be identified under the assumption of short-term stable differences between the birth cohorts in the absence of the Great Recession. A meta-analysis aggregates the estimated effects over six between-cohort comparisons. Furthermore, we conduct a sensitivity analysis to assess the potential consequences if the identification assumption is violated. The findings contribute new evidence on how precipitous and pervasive economic hardship may disrupt youth development by gender and class of origin.

Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this "crash course" accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.

Relying on multiple documents to answer questions is becoming common for both academic and personal inquiry tasks. These tasks often require students to explain phenomena by taking various causal factors that are mentioned separately in different documents and integrating them into a coherent multi-causal explanation of some phenomena. However, inquiry questions may not make this requirement explicit and may instead simply ask students to explain why the phenomenon occurs. This paper explores an "Activity Model Hypothesis" that posits students lack knowledge that their explanation should be multi-causal and how to engage in the kind of thinking needed to construct such an explanation. This experiment, conducted on a sample of eigth grade students, manipulated whether students received a short 10-min lesson on the nature of scientific explanations and multi-causal reasoning. Students who received this causal chain lesson wrote essays that were more causally complex and integrated, and subsequently performed better on an inference verification test, than students who did not receive the lesson. These results point to relatively simple changes to instructions that can provide the support students need for successful multiple-document comprehension.

Given the developmental benefits associated with higher sense of purpose, past work has aimed to understand how experiences of adversity relate to sense of purpose. With a specific focus on experiences of adversity that may impact individuals from marginalized groups, past work has found that discrimination is related to lower sense of purpose in life, but that these effects are weaker for Black adults relative to White adults. The current research aims to extend past work by examining how and for whom discrimination is related to sense of purpose in life. Moreover, the current work also aimed to understand the extent to which sense of purpose spans across generations and whether there are generational differences in the relationship between discrimination and sense of purpose. Using data from the St. Louis Personality and Aging Network study, 822 parents (G1 participants) and 654 children (G2 participants) completed measures for sense of purpose, major experiences of discrimination, and personality traits. Results found mixed evidence for a relationship between discrimination and sense of purpose, with little evidence for consistent moderators. In addition, while the current work found no evidence of intergenerational associations for sense of purpose, results showed that discrimination was positively associated across generations, suggesting a potential for an intergenerational cycle of marginalization.

People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both extinction and protection from extinction when an inhibitory (preventive) cue is present during extinction. In three experiments using the allergist causal learning task, we found that protection could also be achieved by a hidden cause that was inferred but not physically present, so long as that cause was a plausible preventer of the outcome. We additionally showed complete protection by a physically presented cue that was neutral rather than inhibitory at the outset of extinction. Both findings are difficult to reconcile with dominant prediction error theories. However, they are compatible with the idea of theory protection, where the learner attributes the absence of the outcome to the added cue (when present) or to a hidden cause, and therefore does not need to revise their causal beliefs. Our results suggest that prediction error encourages changes in causal beliefs, but the nature of the change is determined by reasoning processes that incorporate existing knowledge of causal mechanisms and may be biased toward preservation of existing beliefs.

Advances in computational language models increasingly enable adaptive support for self-regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves). We compared the accuracy of four Dutch language models (ie, spaCy medium, spaCy large, FastText and ConceptNet NumberBatch) in the context of secondary school students' learning of causal relations from expository texts, scaffolded by causal diagram completion. Since machine learning relies on human-labelled data for the best results, we used a dataset with 10,193 students' causal diagram answers, compiled over a decade of research using a diagram completion intervention to enhance students' monitoring of their text comprehension. The language models were used in combination with four popular machine learning classifiers (ie, logistic regression, random forests, support vector machine and neural networks) to evaluate their performance on automatically scoring students' causal diagrams in terms of the correctness of events and their sequence (ie, the causal structure). Five performance metrics were studied, namely accuracy, precision, recall, F1 and the area under the curve of the receiver operating characteristic (ROC-AUC). The spaCy medium model combined with the neural network classifier achieved the best performance for the correctness of causal events in four of the five metrics, while the ConceptNet NumberBatch model worked best for the correctness of the causal sequence. These evaluation results provide a criterion for model adoption to adaptively support SRL of causal relations in DLEs.

Limited research has examined the active ingredients and mechanisms of change of naturalistic developmental behavioral interventions (NDBIs). The present study used an exploratory sequential mixed-methods design to develop a comprehensive Theory of Change of Project ImPACT, an empirically supported NDBI. We used qualitative data from interviews with intervention experts (n = 10), community providers (n = 22), and caregivers (n = 12) to develop a comprehensive causal model of the intervention process. We then tested select paths of the causal model using path analyses with an archival dataset (n = 92). The causal model described how developmental techniques aimed at supporting children's attention and engagement lay the foundation for more adult-directed learning opportunities and subsequent child skill growth. However, hypothesized causal relationships were not supported by our quantitative analyses. In the future, this research can be used to develop and prioritize nuanced research questions related to the timing, optimization, and mechanistic process underlying NDBIs.

Causal reasoning is important to children's cognition and academic development. However, there have been few empirical studies on the impact of visual cues and non-verbal scaffolding on children's reasoning in continuous causal processes. Hence, the present study aims to explore how causal reasoning in continuous processes is facilitated by visual mind maps and multiple-choice questions through science experiments. By randomly selecting 136 children aged 9-13, the following results were obtained: Children provided with a mind map containing visual causal cues performed significantly better than the non-cue group on explanation tasks regardless of age differences, and children assessed using non-verbal multiple-choice questions scored significantly higher in explaining causal relationships than those using only verbal reports. This suggests that identification and explanation need to be differentiated for a more accurate evaluation of causal reasoning ability. These results have valuable implications for science curriculum and pedagogy at primary schools.

Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects--that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time--when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference.

This study examines the relationship between teachers' technology proficiencies and their level of integrating technology into their lessons. In this research, which was designed in a relational survey model, as data collection tools, the "Technology Proficiency Self-Assessment Scale for 21st Century Learning" developed by Christensen and Knezek and adapted to Turkish conditions by Fidan, Debbag and Çukurbasi (2020); and "Teachers' Technology Integration Indicators Scale" developed by Çakiroglu, Gökoglu and Çebi (2015) were used. The research sample consisted of 398 teachers working in public schools in Istanbul's Pendik and Tuzla districts. The findings showed that teachers' technology proficiencies and integration levels were high. In addition, teachers' technology proficiencies and ability to integrate technology into their lessons did not show statistically significant differences according to their gender, professional seniority and education level. However, the teachers' technology proficiency and the level of integrating technology into their lessons showed statistically significant differences according to their educational status. These differences in both variables were significantly higher for teachers with graduate education than those with undergraduate education. According to another finding from this research, there was a positive, high level and significant relationship between teachers' technology proficiency and their ability to integrate technology into their lessons. As a result of the regression analysis, it was seen that technological proficiency was a significant predictor of technology integration. Technological proficiency explains 53.9% of technology integration.

Explaining natural phenomena by determining causal relationships is conducive to understanding scientific concepts. In science education, numerous studies examine students' causal reasoning. Given the importance of core ideas for students' understanding of how and why a phenomenon occurs, the study focused on the relationship between students' understanding of atoms/molecules interactions and the nature of reasoning. This study drew on a framework that identifies essential components of students' reasoning, which was used to analyze the dissolution phenomenon in the example of salt in water. Students in grades 9-12 (N=147) explained the dissolution of salt. The results showed that there were five types of reasoning: simple descriptive, fuzzy causal, linear causal, interactive causal, and mechanistic. More students in higher than lower grades exhibited non-causal reasoning. Based on the students' drawings of atoms/molecules interactions, the study summarized performance in the association category. Students' performance in drawing indicated that their understanding of particle interactions was limited. The results showed that there was a large correlation between understanding of the core ideas and reasoning types.

This study synthesizes existing research to explore factors affecting student attrition in Thai higher education institutions and develop a causal model for dropout risk. The synthesis uses a mixed-method approach following PRISMA 2020 guidelines, drawing on six years of Thai contextual studies on student attrition, academic intention, commitment, and persistence. Through a systematic review of multiple databases, 21 quantitative or mixed-method studies were identified for inclusion, which yielded 107 items representing 186 occurrences related to student dropout or persistence factors in Thai higher education. These items were grouped into nine clusters: academic integration, attitudinal and behavioral factors, classroom and institutional environment, emotional distress, family support, financial considerations, institutional support, social integration, and student satisfaction. The model synthesizes research findings on student attrition in Thai universities, providing a comprehensive framework for understanding the factors influencing students' persistence and dropout risk. By considering the interplay between these factors, the model aids in developing targeted interventions and informed policy decisions that promote academic success and ensure the long-term efficacy of Thai higher education institutions. The model's application can potentially guide researchers, educators, and policymakers in addressing the challenges students face within the Thai higher education system, ultimately fostering a more supportive and conducive environment for academic achievement.

Entrepreneurial and innovative higher education institutions of the 21st century have become the driving forces of social development, economic growth and global competitiveness by improving human capital through the educational and instructional services they offer, producing qualified scientific information through the scientific research they conduct and by fulfilling an important responsibility in the solution of social problems. In the current study, the effect of higher education on global competition in the context of the transformation of knowledge into economic value in OECD countries was analyzed with the Granger Panel Causality Test. In the application part, a panel data set was created with the data collected from 25 OECD countries for which data were available for the period 2006-2017. The Global Competitiveness Index variable was taken as the basis to reveal the global competitiveness of the countries included in the analysis. In order to reveal the status of their higher education, the gross schooling rate in higher education, the employment rate of the population with higher education, public expenditures on higher education, the quality of the education system, the quality of scientific research institutions, the performance of scientific publications, the R&D expenditures made by higher education and the university-industry cooperation variables were taken as the basis. As a result of the application, it was determined that each variable representing higher education has a bilateral causality relationship in the short term with the Global Competitiveness Index representing global competition.

Including causal links in concept maps enables learners to meaningfully relate concepts to a larger context or problem in terms of how and where concepts apply within the chains of causal events that lead to a given goal or outcome. Given that higher quality maps are produced when students link and sequence events to flow temporally and sequentially in a consistent direction towards a target outcome in a map, it is highly plausible that students can improve learning transfer (the ability to apply concepts to diagnose and solve problems) by including and making more salient the sequences of causal links nestled in the semantic links in concept maps. To lay the groundwork to empirically test this proposition in future research, this study: 1) analyzes 16 concept maps presented in the Proceedings of the 8th Int. Conference on Concept Mapping to codify the diverse and sometimes incongruent syntaxes used to convey causal relationships; 2) examines how the causal link syntaxes combined with semantic links work jointly (or against each other) to create temporal flow; and 3) explore how causal and semantic links can be integrated to increase the saliency and quality of the causal networks connecting concepts to outcomes. A better understanding of how causal links are expressed, integrated, and made more salient in concept maps can reveal ways to help students create concept maps that are more accurate, meaningful, and effective in improving the ability to apply concepts to solve complex problems.

In the rapidly changing atmosphere of the global economy, productivity has become a very important concept for long-term economic growth, development, regional and global competitiveness, raising social living standards and increasing the level of welfare for countries. In the 21st century, when scientific knowledge, technology, innovation, R&D and entrepreneurship manifest themselves in every stage of the production process, human capital has come to the fore as an important and determining factor that increases productivity. In the current study, the effect of human capital, one of the most important outputs of education, on country productivity was analyzed for 24 developing countries, including Turkey, which are in the upper middle income group. In the application part, a panel data set was created for the 24 countries included in the study with the series obtained from the database of PWT10.0 for the period of 1980-2019. The Human Capital Index was used to reveal the human capital status of the countries included in the analysis and the Total Factor Productivity Index was used to reveal the productivity status. The Granger Panel Causality Test was employed to determine whether there is a short-term relationship and the Westerlund Panel Cointegration Test was employed to determine whether there is a long-term relationship between the two variables. As a result of a bilateral causality relationship was found between human capital and productivity in the short-term and a cointegration relationship in the long-term. The study is important and different from other studies in that it focuses on the concepts of human capital and productivity, which have a very limited place in the education literature, although they are directly related to education, and it is grounded on an interdisciplinary approach (bringing together education, sociology and econometrics).

Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A more recent approach is rooted in the potential outcomes framework for causal inference and foregrounds how hypothetical changes in a sample would alter an inference if such cases were otherwise observed (Frank et al., 2008, 2013; Frank & Min, 2007; Xu et al., 2019). One sensitivity measure is the "Impact Threshold of a Confounding Variable," or ITCV, which generates statements about the correlation of an omitted, confounding variable with both a predictor of interest and the outcome (Frank, 2000). The ITCV index can be calculated for any linear model. The "Robustness of an Inference to Replacement," RIR, assesses how replacing a certain percentage of cases with counterfactuals of zero treatment effect could nullify an inference (Frank et al., 2013). The RIR index is more general than the ITCV index.

How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID-19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what "actually" happened (e.g., that a public health policy was implemented and COVID-19 cases declined), but can be made to disagree about the counterfactual, or what "would have" happened otherwise (e.g., whether COVID-19 cases would have declined naturally without intervention) via comparison cases. Across two preregistered studies (total N = 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others' causal judgments and warrant further investigation as a mechanism contributing to belief polarization.

Talent identification and development systems (TIDS) adopt a deterministic perspective (i.e. athletes' future state/performances can be predicted by observations of their initial state/performance), which encourages early identification and specialisation in sport. In this framework, the main aim of sport systems is to enhance predictability and reduce uncertainty, by investigating the causal relationship between entering a talent pathway and becoming an expert performer. Generally, athletes who display ideal body proportions and attain certain performance standards in early developmental stages are labelled as talented (i.e. they display the potential to succeed) and are selected by TIDS, which afford them a superior developmental opportunity (i.e. better training facilities, certified coaching staff, and higher competition levels), to realise their potential. A deterministic approach, thus, (a) sees talent as a fixed capacity, whereby future successful athletes can be identified early (i.e. early identification); and (b) considers entering at an earlier age in a talent pathway a pre-requisite for sporting success (i.e. early specialisation). Contrary to deterministic expectations, recent research has highlighted how being considered talented from the early stages does not guarantee a better likelihood of successfully achieve senior sporting success. In this paper, taking ideas from ecological anthropology, a new probabilistic approach is proposed which considers TIDS as a process (i.e. being talented is about remaining responsive to what you could become), dialogical (i.e. athlete selection and development are not done in isolation, but both influenced by sociocultural constraints), and open-ended (i.e. unpredictability related to future outcomes). If sport systems embrace the uncertainty of developmental pathways opportunities arise to participate in conversations across and within TIDS to support systemic change. We conclude by proposing four key foundations of any probabilistic TIDS: give sport back to kids, ethos of amateurism, delaying selection processes, and early diversification.

This study determines the critical success factors of teaching mathematics to special education (SPED) students wherein a list of success factors is identified through a literature survey and analyzes the causal relationships among the identified factors to evaluate the key success factors using the integration of the grey system theory and decision-making trial and evaluation laboratory (DEMATEL), named as the grey-DEMATEL. Results reveal that professional development (PD), institutional support (IS), and individual factors (IF) are categorized as net causes. They influence other factors considered as net effects, including mentoring, self-efficacy, teaching capacity, student feedback, teaching knowledge, instructional accommodation, and use of technology. Among those net causes, only PD yields as the key factor, while IS and IF are the minor key factors. Thus, decision-makers must allocate resources that provide PD to teachers in delivering SPED mathematics teaching. A sensitivity analysis suggests these findings are robust to linguistic evaluation scale changes. The insights outlined in this study would aid educational managers and decision-makers of educational institutions in carefully designing initiatives to improve the quality of mathematics education provided to SPED students. Some potential directions for future research agenda are also discussed.

Causal asymmetry is a situation where the causal factors under study are more suitable for explaining the outcome than its absence (or vice versa); they do not explain both equally well. In such a situation, presence of a cause leads to presence of the effect, but absence of the cause may not lead to absence of the effect. A conceptual discussion is followed by the empirical example of gaining a degree (or not), comparing the methods logistic regression and Qualitative Comparative Analysis (QCA). While logistic regression, being based on correlational analysis and thus assuming symmetric relationships between variables, does not lend itself automatically to detecting causal asymmetry, it can be used to bring out asymmetry nevertheless. QCA, on the other hand, uncovers asymmetry, if present, by default. The closing recommendation is for researchers to be more aware of the possibility of asymmetry existing, regardless of the particular method employed.

Conventional approaches to causation in the social sciences draw on approaches in the Philosophy of Science in which a causal force acts on cases and generates change in the form of events. This relies on just one of the Aristotelian conceptions of cause - efficient cause - what brings the effect in to being. We should also pay attention to Final Cause - purpose and Formal cause, what makes something what it is and no other. The somethings are complex far from equilbric socio-ecological systems in which human agency has causal powers. This resonates with the understanding of the nature of effect in the complexity frame of reference as the state of the system both in relation to stability and transformation of kind. Effects are systems states. The argument draws on Hegel's and Dewey's understandings of cause / effect relationships as not separable but intimately interwoven. Effects have continuing reciprocal impacts on causes themselves as in positive feedback in systems. This way of thinking about causation allows us to engage with macro social change. The argument will be illustrated by a discussion of the transformation from industrial to post-industrial character across port city regions in high income countries.

Growing literature documents the promise of active learning instruction in engaging students in college classrooms. Accordingly, faculty professional development (PD) programs on active learning have become increasingly popular in postsecondary institutions; yet, quantitative evidence on the effectiveness of these programs is limited. Using administrative data and an instructor fixed effects approach, we estimate the effect of an active learning PD program on student performance and persistence at a large public institution. Findings indicate that the training improved subsequent persistence in the same field. Using a subset of instructors whose instruction was observed by independent observers, we identify a positive association between training and implementation of active learning teaching practices. These findings provide suggestive evidence that active learning PD has the potential to improve student outcomes.

The purpose of this study was to examine the role of the causal connectivity of the statements ("low-medium-high"), elaboration question condition ("focused on the identification of main ideas-focused on the identification of speakers' emotions") and the modality of presentation of discourse ("oral-written") in the comprehension of spontaneous narrative discourse by Spanish-speaking students. With this aim, Argentine college students were asked to listen or read a radio interview, and to answer 4 elaboration questions. These questions were presented after statements that had high causal connectivity and promoted the generation of emotion inferences. Then, they were asked to perform a "free-recall" and a "comprehender's emotions elaboration task." Statements that had high causal connectivity were more often included in the free recall task than those that had low causal connectivity. And, students that elaborated on main ideas included a higher number of statements than those that elaborated on speakers' emotions. In the "comprehenders' emotions elaboration task," students who elaborated on speakers' emotions and read the material included a higher number of statements than those that listened to it. And, the effect of causal connectivity was greater when students read than when they listened to the material.

Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level unmeasured confounding. We focus on one particular ML-based causal inference method based on the targeted maximum likelihood estimation (TMLE) with an ensemble learner called SuperLearner. Through our simulation studies, we observe that training TMLE within groups of similar clusters helps remove bias from cluster-level unmeasured confounders. Also, using within-group propensity scores estimated from fixed effects logistic regression increases the robustness of the proposed within-group TMLE method. Even if the propensity scores are partially misspecified, the within-group TMLE still produces robust ATE estimates due to double robustness with flexible modeling, unlike parametric-based inverse propensity weighting methods. We demonstrate our proposed methods and conduct sensitivity analyses against the number of groups and individual-level unmeasured confounding to evaluate the effect of taking an eighth-grade algebra course on math achievement in the Early Childhood Longitudinal Study.

Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using the individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

Qualitative comparative analysis (QCA) is an empirical research method that has gained some popularity in the social sciences. At the same time, the literature has long been convinced that QCA is prone to committing causal fallacies when confronted with non-causal data. More specifically, beyond a certain case-to-factor ratio, the method is believed to fail in recognizing real data. To reduce that risk, some authors have proposed benchmark tables that put a limit on the number of exogenous factors given a certain number of cases. Many applied researchers looking for methodological guidance have since adhered to these tables. We argue that fears of inferential breakdown in QCA due to an "unfavorable" case-to-factor ratio are without foundation. What is more, we demonstrate that these benchmarks induce more fallacious inferences than they prevent. For valid causal inference, researchers are better off relying on the current state of knowledge in their respective fields.

Superstitious behaviors persist across time, culture, and age. Although often considered irrational and even potentially harmful, superstitions have recently been shown to have positive effects on stress levels, confidence, and ultimately, performance. However, it remains unclear how people conceive of superstitious behaviors, specifically, whether people attribute apparent superstitious efficacy to magical forces or to something else, such as reduced stress or increased confidence. In two studies we asked 6- to 9-year-old children and adults from the United States to rate how causally connected superstitious behaviors were to desired outcomes. In Study 1, participants provided open-ended responses to questions probing why they believed the actions were tied to the outcomes. Participants were more likely across ages to offer natural than magical explanations to explain the connection -- this became more pronounced with age. In Study 2, half the stories did not permit a plausible natural explanation, thus any perceived causal relation could only be attributable to magic. Participants were asked how connected the superstitious behaviors were to the outcomes. We found significant differences in ratings of connectedness between stories that held plausible natural explanations and those that did not in our adult sample only. Our findings suggest that people recruit their naïve psychology when reasoning about the efficacy of superstitious behaviors; however, children may also believe magic to be causally relevant.

This paper analyzes the impact of college athletic reclassification for educational institutions in the United States. Most of America's colleges and universities offer athletic opportunities for their students under NCAA governance. The level of competition and associated resource requirements range from relatively low (Division 3) to high (Division 1). In recent years, there has been an increase in the number of institutions that have either reclassified to a higher division, or publicly considered such a move. The resource obligations of reclassification are significant, and administrators must carefully weigh expected benefits and costs with respect to the institution's mission and vision. While several studies (Bell, 2017; Schwarzber, 2014; Frieder & Fulks, 2007; Tomasini, 2005) have focused on reclassification from Division 1-FCS to FBS--the highest level of competition--less attention has been paid to colleges in Division 2 and 3--which comprise two-thirds of NCAA membership. Our study addresses a gap in the literature by analyzing athletic reclassification among schools moving up from Division 2 to Division 1 (FCS and no-football) during the period of 2001-2018. Drawing upon multiple data sources, we construct a panel of 33 schools (14 treatments and 19 controls) to evaluate the impact of reclassification on applications and athletic department budgets. Results from quasi-experimental methods (difference in differences, event study, and synthetic control models) suggest that reclassification significantly increased basketball program revenues and athletic department expenses among the treatment schools. However, we do not find evidence that reclassification raises student applications in the short term.

Three quantitative methods are compared for their ability to understand different COVID-19 fatality ratios in 33 OECD countries. Linear regression provides a limited overview without sensitivity to the diversity of cases. Cluster Analysis and Dynamic Patterns Synthesis (DPS) gives scrutiny to the granularity of case similarities and differences, and reveals case exceptions. Qualitative Comparative Analysis (QCA) develops causal theory about what conditions are sufficient for explaining outcomes by using robust and transparent conventions. Configurational case-based methods offer important advantages over inferential statistics when there is a need to focus on diversity in small n. These techniques can be combined as multi-methods. DPS and QCA can be used concurrently to aid research insights. These methods are also strengthened by additional qualitative evidence about the cases.

This paper scrutinizes the increasing trend of using international large-scale assessment (ILSA) data for causal inferences in educational research, arguing that such inferences are often tenuous. We explore the complexities of causality within ILSAs, highlighting the methodological constraints that challenge the validity of causal claims derived from these datasets. The analysis begins with an overview of causality in relation to ILSAs, followed by an examination of randomized control trials and quasi-experimental designs. We juxtapose two quasi-experimental studies demonstrating potential against three studies using ILSA data, revealing significant limitations in causal inference. The discussion addresses the ethical and epistemological challenges in applying quasi-experimental designs to ILSAs, emphasizing the difficulty in achieving robust causal inference. The paper concludes by suggesting a framework for critically evaluating quasi-experimental designs using ILSAs, advocating for a cautious approach in employing these data for causal inferences. We call for a reevaluation of methodologies and conceptual frameworks in comparative education, underscoring the need for a multifaceted approach that combines statistical rigor with an understanding of educational contexts and theoretical foundations.