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The thalamus is considered a key region in the neuromechanisms of blepharospasm. However, previous studies considered it as a single, homogeneous structure, disregarding potentially useful information about distinct thalamic nuclei. Herein, we aimed to examine (i) whether grey matter volume differs across thalamic subregions/nuclei in patients with blepharospasm and blepharospasm-oromandibular dystonia; (ii) causal relationships among abnormal thalamic nuclei; and (iii) whether these abnormal features can be used as neuroimaging biomarkers to distinguish patients with blepharospasm from blepharospasm-oromandibular dystonia and those with dystonia from healthy controls. Structural MRI data were collected from 56 patients with blepharospasm, 20 with blepharospasm-oromandibular dystonia and 58 healthy controls. Differences in thalamic nuclei volumes between groups and their relationships to clinical information were analysed in patients with dystonia. Granger causality analysis was employed to explore the causal effects among abnormal thalamic nuclei. Support vector machines were used to test whether these abnormal features could distinguish patients with different forms of dystonia and those with dystonia from healthy controls. Compared with healthy controls, patients with blepharospasm exhibited reduced grey matter volume in the lateral geniculate and pulvinar inferior nuclei, whereas those with blepharospasm-oromandibular dystonia showed decreased grey matter volume in the ventral anterior and ventral lateral anterior nuclei. Atrophy in the pulvinar inferior nucleus in blepharospasm patients and in the ventral lateral anterior nucleus in blepharospasm-oromandibular dystonia patients was negatively correlated with clinical severity and disease duration, respectively. The proposed machine learning scheme yielded a high accuracy in distinguishing blepharospasm patients from healthy controls (accuracy: 0.89), blepharospasm-oromandibular dystonia patients from healthy controls (accuracy: 0.82) and blepharospasm from blepharospasm-oromandibular dystonia patients (accuracy: 0.94). Most importantly, Granger causality analysis revealed that a progressive driving pathway from pulvinar inferior nuclear atrophy extends to lateral geniculate nuclear atrophy and then to ventral lateral anterior nuclear atrophy with increasing clinical severity in patients with blepharospasm. These findings suggest that the pulvinar inferior nucleus in the thalamus is the focal origin of blepharospasm, extending to pulvinar inferior nuclear atrophy and subsequently extending to the ventral lateral anterior nucleus causing involuntary lower facial and masticatory movements known as blepharospasm-oromandibular dystonia. Moreover, our results also provide potential targets for neuromodulation especially deep brain stimulation in patients with blepharospasm and blepharospasm-oromandibular dystonia., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Mediation analysis is a methodology used to understand how and why behavioral phenomena occur. New mediation methods based on the potential outcomes framework are a seminal advancement for mediation analysis because they focus on the causal basis of mediation. Despite the importance of the potential outcomes framework in other fields, the methods are not well known in prevention and other disciplines. The interaction of a treatment (X) and a mediator (M) on an outcome variable (Y) is central to the potential outcomes framework for causal mediation analysis and provides a way to link traditional and modern causal mediation methods. As described in the paper, for a continuous mediator and outcome, if the XM interaction is zero, then potential outcomes estimators of the mediated effect are equal to the traditional model estimators. If the XM interaction is nonzero, the potential outcomes estimators correspond to simple direct and simple mediated contrasts for the treatment and the control groups in traditional mediation analysis. Links between traditional and causal mediation estimators clarify the meaning of potential outcomes framework mediation quantities. A simulation study demonstrates that testing for a XM interaction that is zero in the population can reduce power to detect mediated effects, and ignoring a nonzero XM interaction in the population can also reduce power to detect mediated effects in some situations. We recommend that prevention scientists incorporate evaluation of the XM interaction in their research. [ABSTRACT FROM AUTHOR]

Voter turnout has declined in many countries, raising the question of whether electoral institutions increase voter turnout. We exploit an electoral reform in the Austrian state of Burgenland as a natural experiment to identify the causal effect of polling station opening hours on voter turnout. The results show that a 10% increase in opening hours increased voter turnout by some 0.5 to 0.9 percentage points. The reform also influenced party vote shares. The vote share of the conservative party decreased in the course of the reform, while the vote shares of the other three main parties increased. Conservative voters tend to have an especially strict sense of civic duty and would have participated in the election in any event. Simulations indicate that parliamentary majorities in previous elections would have changed under extended opening hours in favor of the social democratic party. The opening hours of polling stations probably play a more important role in political strategies than recognized to date. [ABSTRACT FROM AUTHOR]

Dam construction and operation modify land systems. We synthesized 178 observations of dam-induced land system changes from 54 peer-reviewed case studies. Changing extents of forests (23%), agricultural land (21%), and built-up areas (11%) were reported frequently, alongside alterations in land use intensity (23%). Land cover changes were mostly related to hydropower and multi-purpose dams, while irrigation dams dominantly caused land use intensity changes. While a significant share of the changes was caused by reservoir flooding (29%), indirect effects which interact with societal and environmental systems (42%) were of utmost importance. We suggested the distance to the dam and the time since commissioning as potential controls for the direction of land system changes. Our insights provide opportunities for future inductive investigations across large populations of dams at regional to global scales and highlight that multi-disciplinary research perspectives are imperative for the production of generalizable knowledge. [ABSTRACT FROM AUTHOR]

This study estimates causal effects of the governor vacancy on the regional economy, exploiting the case of exogenous governor vacancy in Korea. We find that the governor vacancy has a negative impact on the regional economy by lowering the employment rate and reducing the amount of credit card expenditures. Negative effects are more pronounced among vulnerable groups of the labor market such as women and aged 20∼29 and 50∼ 59. In addition, negative effects vary by characteristics of the governor. Negative effects of the governor vacancy on the regional economy show empirical evidence suggesting that leaders do matter. [ABSTRACT FROM AUTHOR]

• Causes of crop booms investigated to help prevent socio-ecological degradation • Examined spatial pattern of maize boom in Sayaboury Province using land system model • Accessibility of trader companies, slope and soil suitability significantly contributed • Tested effects of market access, crop profitability, productivity increases and farm gate price • Temporal changes in profitability and productivity increases had strongest effect in explaining the spatial extent of maize boom Crop booms are phenomena of global environmental change that keep on occurring around the globe and frequently exploit or degrade the local socio-ecological resources (resulting in e.g. loss of biodiversity, soil erosion, indebtedness). While causal mechanisms were identified and summarized in several frameworks, the causal effects of the identified factors remained largely unknown. In this study, we set up a new application of a spatial land system model to examine the causes for the clustered spatial pattern of the maize boom between 2000 and 2016 in Sayaboury Province, Laos. The factors tested included market access (travel time to trader companies), land productivity and total net revenue (proxy for profitability), spatial differences in farm gate price of maize, slope, and soil types. While crop booms are commonly associated with high commodity prices and improved market accessibility, our simulation results suggested that the combination of the geographic and economic factors we tested partially contribute to explain the location and spatial extent of the maize boom, but a full explanation has not been found. Interestingly though, temporal dynamics, such as increases in land productivity and profitability had the largest effect on model performance regarding the size of the maize boom area (experiment 2). Productivity and profitability increased thanks to political economic support for the introduction of a series of techniques (i.e. hybrid maize cultivars, herbicides, mechanical tillage and sowing) that made maize mono-cropping disproportionally competitive over other land management. We outline implications of our findings for governance bodies that are faced with crop booms. [ABSTRACT FROM AUTHOR]

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Objective: To explore whether there is a genetic causal relationship between sleep traits and the risk of autoimmune arthritis (AA)., Methods: Univariable and multivariable Mendelian randomization was employed using genome-wide association studies data to assess sleep traits' associations with AAs, including rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis. The inverse-variance weighted method served as the primary analysis, supplemented by the CAUSE method to improve power and mitigate false positives. Mediation Mendelian randomization was used to quantify direct and indirect effects., Results: Significant associations were shown between insomnia symptoms and increased risk of overall RA (odds ratio = 2.75, 95% confidence interval 1.45-5.22) and seronegative RA (odds ratio = 6.95, 95% confidence interval 2.47-19.56). CAUSE results revealed an association of insomnia symptoms with overall RA and seronegative RA, as well as the sleep duration with overall RA. After the adjustment for body mass index, alcohol status, smoking status, and physical activity levels, multivariable analyses revealed that genetic predisposition to insomnia symptoms and prolonged sleep duration showed independent negative associations with the risk of overall RA and seropositive RA. In the reversed multivariable analyses, a borderline negative association was shown in the overall RA-sleep duration and a positive association of seropositive RA with the risk of insomnia symptoms., Conclusion: This study demonstrated a potential bidirectional causal relationship that genetic predisposition to insomnia symptoms and shorter sleep duration was associated with the risk of AA, especially RA. Genetic predisposition to RA was also associated with decreased sleep duration, as well as increased insomnia symptom risk., Competing Interests: Declaration of conflicts of interest The authors declare that they do not have any competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In this issue of the Causal, Casual, and Curious column, I compare several ways of extracting information from post-treatment variables and call attention to some peculiar relationships among them. In particular, I contrast do-calculus conditioning with counterfactual conditioning and discuss their interpretations and scopes of applications. These relationships have come up in conversations with readers, students and curious colleagues, so I will present them in a question–answers format.

Summary: Effect modification means the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if this pattern is noted if it occurs. We propose a new strategy, called the submax‐method, that combines exploratory, and confirmatory efforts to determine whether there is stronger evidence of causality—that is, greater insensitivity to unmeasured confounding—in some subgroups of individuals. It uses the joint distribution of test statistics that split the data in various ways based on certain observed covariates. For L binary covariates, the method splits the population L times into two subpopulations, perhaps first men and women, perhaps then smokers and nonsmokers, computing a test statistic from each subpopulation, and appends the test statistic for the whole population, making 2L+1 test statistics in total. Although L binary covariates define 2L interaction groups, only 2L+1 tests are performed, and at least L+1 of these tests use at least half of the data. The submax‐method achieves the highest design sensitivity and the highest Bahadur efficiency of its component tests. Moreover, the form of the test is sufficiently tractable that its large sample power may be studied analytically. The simulation suggests that the submax method exhibits superior performance, in comparison with an approach using CART, when there is effect modification of moderate size. Using data from the NHANES I epidemiologic follow‐up survey, an observational study of the effects of physical activity on survival is used to illustrate the method. The method is implemented in the R package submax which contains the NHANES example. An online Appendix provides simulation results and further analysis of the example. [ABSTRACT FROM AUTHOR]

One of the basic aims of science is to unravel the chain of cause and effect of particular systems. Especially for large systems, this can be a daunting task. Detailed interventional and randomized data sampling approaches can be used to resolve the causality question, but for many systems, such interventions are impossible or too costly to obtain. Recently, Maathuis et al. (2010), following ideas from Spirtes et al. (2000), introduced a framework to estimate causal effects in large scale Gaussian systems. By describing the causal network as a directed acyclic graph it is a possible to estimate a class of Markov equivalent systems that describe the underlying causal interactions consistently, even for non-Gaussian systems. In these systems, causal effects stop being linear and cannot be described any more by a single coefficient. In this paper, we derive the general functional form of a causal effect in a large subclass of non-Gaussian distributions, called the non-paranormal. We also derive a convenient approximation, which can be used effectively in estimation. We show that the estimate is consistent under certain conditions and we apply the method to an observational gene expression dataset of the Arabidopsis thaliana circadian clock system. [ABSTRACT FROM AUTHOR]

Weak instruments produce causal inferences that are sensitive to small failures of the assumptions underlying an instrumental variable, so strong instruments are preferred. The possibility of strengthening an instrument at the price of a reduced sample size has been proposed in the statistical literature and used in the medical literature, but there has not been a theoretical study of the trade-off of instrument strength and sample size. This trade-off and related questions are examined using the Bahadur efficiency of a test or a sensitivity analysis. A moderate increase in instrument strength is worth more than an enormous increase in sample size. This is true with a flawless instrument, and the difference is more pronounced when allowance is made for small unmeasured biases in the instrument. A new method of strengthening an instrument is proposed: it discards half the sample to learn empirically where the instrument is strong, then discards part of the remaining half to avoid areas where the instrument is weak; however, the gains in instrument strength can more than compensate for the loss of sample size. The example is drawn from a study of the effectiveness of high-level neonatal intensive care units in saving the lives of premature infants. [ABSTRACT FROM AUTHOR]

Abstract: In a clinical trial, statistical reports are typically concerned about the mean difference in two groups. Now there is increasing interest in the heterogeneity of the treatment effect, which has important implications in treatment evaluation and selection. The treatment harm rate (THR), which is defined by the proportion of people who has a worse outcome on the treatment compared to the control, was used to characterize the heterogeneity. Since THR involves the joint distribution of the two potential outcomes, it cannot be identified without further assumptions even in the randomized trials. We can only derive the simple bounds with the observed data. But the simple bounds are usually too wide. In this paper, we use a secondary outcome that satisfies the monotonicity assumption to tighten the bounds. It is shown that the bounds we derive cannot be wider than the simple bounds. We also construct some simulation studies to assess the performance of our bounds in finite sample. The results show that a secondary outcome, which is more closely related to the primary outcome, can lead to narrower bounds. Finally, we illustrate the application of the proposed bounds in a randomized clinical trial of determining whether the intensive glycemia could reduce the risk of development or progression of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic graphs (DAGs), maximal ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion unifies covariate adjustment for a large set of graph classes. Moreover, we define an explicit set that satisfies our criterion, if there is any set that satisfies our criterion. We also give efficient algorithms for constructing all sets that fulfill our criterion, implemented in the R package dagitty. Finally, we discuss the relationship between our criterion and other criteria for adjustment, and we provide new soundness and completeness proofs for the adjustment criterion for DAGs. [ABSTRACT FROM AUTHOR]

Data collected on 2638 kids originated from 1305 dams and 197 sires during 1979 to 2012 in Raeini Cashmere goat breeding station located in Baft, Kerman province, south-eastern part of Iran, on body weights of 2638 Raeini Cashmere kids at birth (BW), at weaning (WW) and at six-month age (6 MW) were used to adopt a fully recursive multivariate model considering causal effects from BW on WW and on 6 MW and from WW on 6 MW. The adopted recursive multivariate model (RMM) was compared with a standard multivariate mixed model (SMM), in which causal relationships were ignored, in terms of deviance information criterion (DIC), predictive ability and the estimates of genetic and phenotypic parameters. Preliminary analyses revealed that the importance of direct additive genetic, maternal permanent environmental and temporary environmental effects for genetic evaluation of all the studied traits. Maternal additive genetic effects were only influencing on BW. Model comparisons via DIC revealed the plausibility of RMM over SMM; DIC value obtained by fitting RMM (-15,218.76) was lower than that of obtained under SMM (21,654.92). For BW, lower mean square of error (MSE) and higher Pearson's correlation coefficient between the observed and predicted values (r(y, y ^ )) were obtained under SMM but in the case of WW and 6 MW lower MSE and higher r(y, y ^ ) were obtained under RMM. The causal effects of BW on WW and on 6 MW and that of WW on 6 MW were 1.94 kg, 2.48 kg and 1.03 kg, respectively. The estimates of direct heritability, maternal heritability (for BW) and ratio of permanent and / or temporary environmental variances to phenotypic variance were not significantly affected by model fitted (SMM and RMM). Direct additive genetic correlations of BW-WW and BW-6 MW were not statistically significant under SMM (95% HPD interval included zero) but the corresponding estimates under RMM were significant (99% HPD interval did not include zero) values of 0.28 and 0.84, respectively; implying that genes associated with BW also increase WW and 6 MW in Raeini Cashmere goat. Direct genetic correlation estimate for WW-6 MW were 0.88 (under SMM) and 0.29 (under RMM). Generally, RMMs perform better than SMMs and may provide a better explanation to biological relationships among the studied traits. [ABSTRACT FROM AUTHOR]

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA. [ABSTRACT FROM AUTHOR]

This paper evaluates the effects of the London Cycle Superhighways (CS) on the usage of the London Cycle Hire. A total of 52 km CS routes and 762 cycle hire docking stations are included for a period of 5 years. Covariates such as land use, road network characteristics and socio-economic information are included in the data set. Transaction records of each docking station are obtained from Transport for London. We first estimate the effect area of the CS routes using a buffer-area based regression. Our results suggest that the effect area of the London Cycle Superhighways is 300 m from either side of the center of the CS routes. Then the propensity score matching methods are applied to select control docking stations and estimate the effects of the CS routes on the cycle hire usage. The results suggest a significant increase of 27.1% in the average ridership for docking stations within the effect area. For trips with both the origin and destination in the effect area, the increase is 73.2%. We also investigate the effects of the cycle superhighways on average travel speed and travel time of cycle hire trips. The travel speed using cycle hires is increased by 1.16 km/h (13.3%), although the effect is insignificant for trips with destinations out of the effect area. The cycle superhighways also significantly reduced the travel time by 11%. The results show no significant effects on the travel distance due to data restriction. [ABSTRACT FROM AUTHOR]

There is now a critical threshold of evidence documenting the relationship between one's exposure to shocks in early life and outcomes in adulthood. Using Nepal's vitamin A supplementation program, this study provides additional insights on the mechanisms and differences in effects by the timing of the first exposure and across different groups. The program's sequential rollout between 1993 and 2001 and the age eligibility provide an exogenous variation in exposure to the program. I utilize that variation and data on more than 700,000 individuals from the 2011 census to estimate the program's causal effect on long-term health and educational outcomes. I find that the program reduced the probability of having a disability but had no effect on educational outcomes. The positive effects on disability seem to have improved marriage prospects. The program also had different effects on individuals based on their timing of the exposure to the program, with an early exposure strengthening the positive health effects. Effects also differed by the individual's gender and ethnicity. They were more pronounced for men and individuals from traditionally advantaged ethnic groups. The findings suggest that long-term effects can differ even when the take-up rate is similar for different segments of the population. [ABSTRACT FROM AUTHOR]

Conventional wisdom dictates that the more we know about a problem domain the easier it is to predict the effects of policies in that domain. Strangely, this wisdom is not sanctioned by formal analysis, when the notions of “knowledge” and “policy” are given concrete definitions in the context of nonparametric causal analysis. This note describes this peculiarity and speculates on its implications.

This paper describes the influence of government ideology on economic policy-making in the United States. I review studies using data for the national, state and local levels and elaborate on checks and balances, especially divided government, measurement of government ideology and empirical strategies to identify causal effects. Many studies conclude that parties do matter in the United States. Democratic presidents generate, for example, higher rates of economic growth than Republican presidents, but these studies using data for the national level do not identify causal effects. Ideology-induced policies are prevalent at the state level: Democratic governors implement somewhat more expansionary and liberal policies than Republican governors. At the local level, government ideology hardly influences economic policy-making at all. How growing political polarization and demographic change will influence the effects of government ideology on economic policy-making will be an important issue for future research. [ABSTRACT FROM AUTHOR]

This study evaluated the policy impact of the Gyeonggi innovation school movement. Of primary concern was whether or not the effect of the school reform observed from the survey data might be due to selection bias, that is, if the policy effect would be confounded with other covariates. Thus based on the analytical framework of the Rubin causal model, this study estimated causal effects of Gyeonggi innovation schools. From the results of multilevel analysis with matched high schools and weighting individual observations of Gyeonggi Education Panel Study 2012 (GEPS 2012) by propensity scores, this study did not find positive evidence of Gyeonggi innovation schools on academic achievement. However students' school satisfaction was much higher in innovation schools than their counterparts. [ABSTRACT FROM AUTHOR]

The floating population has become the main driver of urban population excessive growth in China's mega cities. Urban transit system (UTS) is a significant factor in population spatial distributions within urban areas, especially rapid and high-capacity transit systems. This paper analyzes the causal effects of the extension of expressways and subways between 2000 and 2010 in the Beijing Metropolitan Area (BMA), focusing on the group differences between the local residents and the floating population. Due to the endogeneity of transportation improvements and population growth, Instrumental Variable (IV) regression model is applied to avoid this problem. The results show the local residents increased in the inner suburbs but decreased in the city center, while the floating population increased in the majority areas. IV regression results show that the extension of urban transit systems had statistically significant impacts on population growth across the BMA. The results also show that the extension of urban subway system had more effects on the floating population than the local residents across the BMA. It is mainly caused by the rather low fare of urban subway system. This implies that the excessive subsidy on urban subway system could result in excessive floating population growth and residential differentiation, even residential segregation. Hence, it is necessary to plan and design reasonable and scientific urban transit systems in order to advance reasonable population size and promote residential integration. Moreover, the regional analysis shows that the effects of urban transportation improvements on the local residents are not statistically significant in the inner suburbs. It implies that urban transportation improvements had limited effects on inducing people to move to suburban areas and controlling center city's population in Beijing. Therefore, it should be stressed the differentiated effects of urban transportation improvements on population distribution in the process of urban planning and population control. [ABSTRACT FROM AUTHOR]

Background: Insomnia symptoms are associated with type 2 diabetes incidence but are also associated with a range of potential time-varying covariates which may confound and/or mediate associations. We aimed to assess whether cumulative exposure to insomnia symptoms has a causal effect on type 2 diabetes incidence. Methods: A prospective cohort study in the West of Scotland, following respondents for 20 years from age 36. 996 respondents were free of diabetes at baseline and had valid data from up to four follow-up visits. Type 2 diabetes was assessed at the final visit by self-report, taking diabetic medication, or blood-test (HbA1c ≥ 6.5% or 48 mmol/ mol). Effects of cumulative insomnia exposure on type 2 diabetes incidence were estimated with traditional regression and marginal structural models, adjusting for time-dependent confounding (smoking, diet, physical inactivity, obesity, heavy drinking, psychiatric distress) as well as for gender and baseline occupational class. Results: Traditional regression yielded an odds ratio (OR) of 1.34 (95% CI: 1.06-1.70) for type 2 diabetes incidence for each additional survey wave in which insomnia was reported. Marginal structural models adjusted for prior covariates (assuming concurrently measured covariates were potential mediators), reduced this OR to 1.20 (95% CI: 0.98-1.46), and when concurrent covariates were also included (viewing them as potential confounders) this dropped further to 1.08 (95% CI: 0.85-1.37). Conclusions: The association between cumulative experience of insomnia and type 2 diabetes incidence appeared confounded. Evidence for a residual causal effect depended on assumptions as to whether concurrently measured covariates were confounders or mediators. [ABSTRACT FROM AUTHOR]

Few studies have examined the causal relationship between chronic exposure to air pollutants during pregnancy and depression in adolescent offspring. In addition, it has not been investigated whether exposure is most harmful to adolescents in certain populations and at certain stages of pregnancy. A total of 1975 adolescents from 1632 families from the China Family Panel Study, a representative national longitudinal cohort, were included in this study. We used high-resolution satellite retrieval data to assess the PM 2.5 exposure of mothers during pregnancy. Specifically, we employed a two-stage instrumental variable model (IV-2SLS) within the counterfactual causal inference framework, and selected and validated appropriate instruments, thereby mitigating potentially biased results arising from bi-direction between dependent and independent variables. This approach allowed us to explore the causal relationship between maternal PM 2.5 exposure during pregnancy and adolescent depression symptoms. The endogeneity of air pollution during pregnancy and the need for a causal model were suggested by the results of the model comparisons. Using the IV-2SLS model, we found that maternal exposure to PM 2.5 during pregnancy exacerbates depressive symptoms in the offspring during adolescence (β = 0.2, 95% CI: 0.05–0.34). We also found that exposure during the first trimester may cause greater harm. Adolescents with low household income, being male, irregular exercise habits, living in rural areas, and having mothers with poorer mental status may be more vulnerable. The findings suggest that maternal exposure to PM 2.5 during pregnancy may have a negative impact on the depression symptoms of offspring in adolescence and that more attention should be paid to vulnerable populations and the window of vulnerability. • PM 2.5 exposure during pregnancy affects depressive symptoms in adolescents. • The first trimester is a susceptible window for PM 2.5 exposure during pregnancy. • Sensitive populations identified for PM 2.5 exposure during pregnancy. • The causal model was used to avoid endogeneity. [ABSTRACT FROM AUTHOR]

Effect modification and interaction are key concepts within epidemiologic research. They refer to situations where the magnitude and/or direction of the causal effect of some exposure variable on an outcome depends on the level of a second variable (effect modification) or on the effect of a second variable (interaction). Interest in case of effect modification is primarily in one exposure variable, with its effects varying across subgroups, whereas primary interest in case of interaction is in the interplay of effects of two exposure variables. Distinctions between the concepts of effect modification and interaction are subtle. The goal of this article is to clarify these distinctions by using the concept of mediation, which focuses on elucidating mechanisms of causal effects., Competing Interests: Declaration of competing interest The author is a member of the editorial board of the Journal of Clinical Epidemiology but was not involved in the peer review process or in the decision to publish., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Inferring causal effects between variables when utilizing observational data is challenging due to confounding factors not controlled through a randomized experiment. Propensity score matching can decrease confounding in observational studies and offers insights about potential causal effects of prophylactic management interventions such as vaccinations. The objective of this study was to determine potential causality and impact of vaccination with an Escherichia coli J5 bacterin on the productive performance of dairy cows applying propensity score matching techniques to farm-recorded (e.g., observational) data. Traits of interest included 305-d milk yield (MY305), 305-d fat yield (FY305), 305-d protein yield (PY305), and somatic cell score (SCS). Records from 6,418 lactations generated by 5,121 animals were available for the analysis. Vaccination status of each animal was obtained from producer-recorded information. Confounding variables considered were herd-year-season groups (56 levels), parity (5 levels: 1, 2, 3, 4, and ≥5), and genetic quartile groups (4 levels: top 25% through bottom 25%) derived from genetic predictions for MY305, FY305, PY305, and SCS, as well as for the genetic susceptibility to mastitis. A logistic regression model was applied to estimate the propensity score (PS) for each cow. Subsequently, PS values were used to form pairs of animals (1 vaccinated with 1 unvaccinated control), depending on their PS similarities (difference in PS values of cows within a match required to be <20% of 1 standard deviation of the logit of PS). After the matching process, 2,091 pairs of animals (4,182 records) remained available to infer the causal effects of vaccinating dairy cows with the E. coli J5 bacterin. Causal effects estimation was performed using 2 approaches: simple matching and a bias-corrected matching. According to the PS methodology, causal effects of vaccinating dairy cows with a J5 bacterin on their productive performance were identified for MY305. The simple matched estimator suggested that vaccinated cows produced 163.89 kg more milk over an entire lactation when compared with nonvaccinated counterparts, whereas the bias-corrected estimator suggested that such increment in milk production was of 150.48 kg. Conversely, no causal effects of immunizing dairy cows with a J5 bacterin were identified for FY305, PY305, or SCS. In conclusion, the utilization of PS matching techniques applied to farm-recorded data was feasible and allowed us to identify that vaccination with an E. coli J5 bacterin relates to an overall milk production increment without compromising milk quality., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Previous observational research has documented an association between age at first childbirth (AFB) and postpartum depression (PPD). However, the causal relationship remains unclear. This study aimed to assess the causal effects of AFB on PPD using a two-sample Mendelian randomization (MR) analysis., Methods: Three sets of instrumental variables were obtained from the United Kingdom Biobank (UK Biobank), Neale Lab consortium and a meta-analysis of genome-wide association studies (GWAS). Single-nucleotide polymorphisms (SNPs) associated with the PPD phenotype were obtained from the Finngen consortium, which included 13,657 cases and 236,178 controls. Inverse variance weighted (IVW), weighted median, weighted mode, and MR-Egger methods to evaluate causal effects. Heterogeneity was assessed using Cochran's Q test and funnel plots. Horizontal pleiotropy and sensitivity were assessed using the MR-Egger intercept test and "leave-one-out" analysis, respectively. Further meta-analysis was performed to validate the robustness of this relationship. Additionally, the potential mediating effects of risk factors associated with PPD were analyzed., Results: Strong causal effects between AFB and PPD was found in both IVW and weighted median methods, which was further supported by meta-analysis (IVW, odds ratio [OR] 0.59 [95% confidence interval (CI) 0.36-0.96, p = 0.03]; weighted median, OR 0.59 [95% CI 0.37-0.95, p = 0.03]). The power of the MR supports the robustness of the findings. Heterogeneity or horizontal pleiotropy was not observed. Major depressive disorders, family income levels, and marital stress were identified as potential mediating factors in the causal relationships., Conclusion: Results of MR analysis supported the causal effect of increased AFB in reducing the risk for PPD., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called “transportability”, defined as a license to transfer information learned in experimental studies to a different population, on which only observational studies can be conducted. Given a set of assumptions concerning commonalities and differences between the two populations, Pearl and Bareinboim [1] derived sufficient conditions that permit such transfer to take place. This article summarizes their findings and supplements them with an effective procedure for deciding when and how transportability is feasible. It establishes a necessary and sufficient condition for deciding when causal effects in the target population are estimable from both the statistical information available and the causal information transferred from the experiments. The article further provides a complete algorithm for computing the transport formula, that is, a way of combining observational and experimental information to synthesize bias-free estimate of the desired causal relation. Finally, the article examines the differences between transportability and other variants of generalizability.

• A systematic approach for estimating causal effects of safety countermeasures is proposed. • The proposed approach aims to better combine prior knowledge with data. • The causal inference model provides helpful guidance for research design and data collection. • An empirical study was conducted to illustrate how the approach accurately estimates the strict causality between factors and how it helps researchers understand the complex mechanisms behind safety phenomena. Although the research into causal effects has always been the core part of safety science, the formal mathematical method to infer causal effects is quite new. With the rapid development of big data technology, safety research based on experience changes to data-driven research. This change improves the reliability and accuracy of safety decision-making to a certain extent. However, researchers pay too much attention to the amount of data and computing power, accelerating the separation of prior knowledge and data. We proposed a systematic method suitable for safety science to carry out quantitative causal inference to study causal problems such as the effectiveness of safety countermeasures. This method aims to integrate safety-related prior knowledge and data organically and provide essential guidance for research design and data collection. Finally, an empirical study was conducted to illustrate how the approach can accurately estimate the rigorous causal relationship between factors and how it can help researchers understand the complex causal mechanism behind the safety or accident phenomenon. [ABSTRACT FROM AUTHOR]

Air pollution remains a risk factor for the global burden of disease. Middle-aged and older people are more susceptible to air pollution because of their declining physical function and are more likely to develop diseases from long-term air pollution exposure. Studies of the effects of air pollution on cognitive function in middle-aged and older adults have been inconsistent. More representative and definitive evidence is needed. This study analysed data from the Chinese Family Panel Study, an ongoing nationwide prospective cohort study, collected in waves 2014, 2016 and 2018. Rigorously tested instrument was selected for analysis and participants' PM 2.5 and instrument exposures were assessed using high-precision satellite data. The causal relationship between long-term exposure to air pollution and poor cognitive function in middle-aged and older adults was investigated using the Correlated Random Effects Control Function (CRE-CF) method within a quasi-experimental framework. This study included a total of 7042 participants aged 45 years or older. A comparison of CRE-CF with other models (OLS model, ordered probit model, and ordered probit-CRE model) demonstrated the necessity of using CRE-CF given the endogeneity of air pollution. The credibility and validity of the instrumental variable were verified. In the CRE-CF model, long-term exposure to PM 2.5 was found to accelerate cognitive decline in middle-aged and older adults (coefficients of −0.159, −0.336 and −0.244 for the total cognitive, verbal and mathematical scores, respectively). Taken together, these results suggest that chronic exposure to ambient air pollution is associated with cognitive decline in middle-aged and older adults, which highlights the need for appropriate protective policies. [Display omitted] • The adverse effects of PM2.5 on cognitive ability were identified. • Evidence from a representative Chinese national cohort study. • A rigorously validated causal model is used to get robust results. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to explore the causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms in an elderly Chinese population., Design, Setting and Analysis: Cross-sectional, baseline data were obtained from the China Longitudinal Ageing Social Survey, a stratified, multistage, probabilistic sampling survey conducted in 2014 that covers 28 of 31 provincial areas in China. The causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms were analysed using the conditional average treatment effect method of machine learning. The causal effects model's adjustment was made for age, gender, residence, marital status, educational level, ethnicity, wealth quantile and other factors., Outcome: Assessment of the causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms., Participants: 7496 subjects who were 60 years of age or older and who answered the questions on depressive symptoms and other independent variables of interest in a survey conducted in 2014 were included in this study., Results: Physical disability and number of comorbid chronic diseases had causal effects on depressive symptoms. Among the subjects who had one or more functional limitations, the probability of depressive symptoms increased by 22% (95% CI 19% to 24%). For the subjects who had one chronic disease and those who had two or more chronic diseases, the possibility of depressive symptoms increased by 13% (95% CI 10% to 15%) and 20% (95% CI 18% to 22%), respectively., Conclusion: This study provides evidence that the presence of one or more functional limitations affects the occurrence of depressive symptoms among elderly people. The findings of our study are of value in developing programmes that are designed to identify elderly individuals who have physical disabilities or comorbid chronic diseases to provide early intervention., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

This paper employs a difference-in-differences strategy to examine the causal effect of exposure to the COVID-19 pandemic on interpersonal trust amidst zero-COVID policies in China. Using a nationally representative panel survey, we find that COVID-19 exposure leads to a decrease in the levels of generalized trust. We also show that the change in interpersonal trust varies across domains. Specifically, COVID-19 exposure significantly decreases trust in parents, neighbors, and local government officials, but has small and insignificant effects on trust in doctors, strangers, and Americans. Empirical tests suggest that changes in income and physical health status are not likely to be potential channels. We provide some evidence for the mechanism of deteriorated mental health status and pessimistic expectations., Competing Interests: The authors declare that they have no conflict of interest., (© 2023 Elsevier Inc. All rights reserved.)

This paper evaluates the effects of the London Cycle Superhighways (CS) on cycle collisions. A total of 45 CS segments and 375 control segments are observed for a period of 8 years in London. Variables such as road characteristics, crash history and socio-economic information are included in the data set. Traffic characteristics including traffic volume, cycle volume and traffic speed are obtained from Department for Transport. We first estimate the safety effects on the CS routes using Empirical Bayes methods. Then propensity score matching methods are also applied for comparison. The introduction of cycle superhighways caused cycling traffic volumes to increase dramatically along CS routes with no significant impacts on collision rates. Our models find that the increase in traffic was associated with a rise in annual total cycle collisions of around 2.6 per km (38% in percentage). However, when we re-estimate the effects based on cycle collision rates rather than levels, our results also show that the CS routes are not more dangerous or safer than the control roads. Among the four CS routes, CS3 performs the best in protecting cyclists with a large proportion of segregated lanes whilst the cyclists have to share the lanes with motorists on other routes. It is recommended that consistent safety designs should be applied on all CS routes for a safer cycling environment. [ABSTRACT FROM AUTHOR]