Direction Dependence Analysis in the Presence of Confounders: Applications to Linear Mediation Models Using Observational Data

Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.

Social anxiety of the citizens of Kazakhstan: The dynamics of change and its impact on public consciousness

The purpose of the study is to analyze the factors associated with the formation of social anxiety and to explore trends in their impact on society in the Republic of Kazakhstan. The authors have used comparative, descriptive, and deductive methods to achieve the research goals. The results of the study determined that anxiety phenomena have become increasingly common over time and social anxiety is one of the most dangerous due to its degree of limitation. The vast majority of people experience some form of social anxiety, which occurs when distorted reality intervenes and certain moments signalize as dangerous. As a product of individual experience and sociopolitical construct, fear becomes the element organizing the state order. The social aspects are notably relevant to the process when the common sense of public consciousness puts security in the foreground as a matter of the greatest importance and urgency.

Covariate selection in causal learning under non-Gaussianity

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model. The present study introduces a non-Gaussian method for covariate selection and provides a forward selection algorithm for linear models (i.e., non-Gaussian forward selection; nGFS) to select appropriate covariates from a set of potential control variables to avoid inconsistent and biased estimators of the causal effect of interest. Further, we demonstrate that the forward selection algorithm has properties compatible with principles of direction of dependence, i.e., probing whether the causal target model is correctly specified with respect to the causal direction of effects. Results of a Monte Carlo simulation study suggest that the selection algorithm performs well, in particular when sample sizes are large (i.e., n ≥ 250) and data strongly deviate from Gaussianity (e.g., distributions with skewness beyond 1.5). An empirical example is given for illustrative purposes.

The mediating effect of resilience on pregnancy stress and prenatal anxiety in pregnant women

Objective

To investigate the relationship between pregnancy stress and prenatal anxiety in pregnant women in Urumqi, Xinjiang, and the mediating effect of mental resilience level on the relationship between pregnancy stress and prenatal anxiety.

Method

The investigation involved 750 pregnant women at a tertiary hospital in Urumqi, and included a questionnaire eliciting general demographic information, a pregnancy stress scale (Pregnancy Pressure Scale, PPS), generalized anxiety disorder scale (Generalized Anxiety Disorder-7, GAD-7), and a mental resilience scale (Connor-Davidson resilience scale, CD-RISC). The Bootstrap mediation effect test was used to test the effect relationship between variables, and Amos was used to establish the structural equation model.

Results

Among the 750 participants, 122 (16.2%) had moderate or greater pregnancy stress (PPS > 1), 372 (49.6%) had mild or greater anxiety symptoms (GAD-7 > 5), and 241 (32.1%) had good or higher mental resilience score. Pregnancy stress negatively affected resilience (β = -0.37, p < 0.01), and resilience also negatively affected prenatal anxiety (β = -0.12, p < 0.01). The mediating effect value of resilience was 8.3%.

Conclusion

Pregnancy stress, mental resilience, and prenatal anxiety were significantly correlated, and mental resilience played a partial mediating role in the influence of pregnancy stress on prenatal anxiety. It is recommended that pregnant women exercise their mental resilience to reduce the incidence of prenatal anxiety and promote physical and mental health.

Exploring the Relationship Between Interpersonal Emotion Regulation and Social Anxiety Symptoms: The Mediating Role of Negative Mood Regulation Expectancies

Background

This study aimed to investigate the relationship between interpersonal emotion regulation (IER) and social anxiety symptoms and the mediating role of negative mood regulation expectancies (NMRE). We hypothesised that IER is positively associated with social anxiety symptoms, controlling for depression and intrapersonal emotion regulation strategies of suppression and reappraisal, and NMRE mediate this relationship.

Methods

Study 1 was conducted with a student sample (N = 400) and Study 2 included a community sample with 271 participants.

Results

Study 1 showed that, of four IER strategies, soothing and social modeling were positively, and perspective-taking was negatively related to social anxiety symptoms controlling for depression, suppression and reappraisal. Study 2 replicated these findings and extended them by showing the mediated relationship between the two IER strategies (i.e. enhancing positive affect and soothing) and social anxiety symptoms through NMRE.

Conclusions

The results contribute to the limited research on IER by portraying its relationship with social anxiety symptoms and revealing the mediating role of NMRE in this relationship.

Conditional Direction Dependence Analysis: Evaluating the Causal Direction of Effects in Linear Models with Interaction Terms

Direction dependence analysis (DDA) makes use of higher than second moment information of variables (x and y) to detect potential confounding and to probe the causal direction of linear variable relations (i.e., whether x → y or y → x better approximates the underlying causal mechanism). The "true" predictor is assumed to be a continuous nonnormal exogenous variable. Existing methods compatible with DDA, however, are of limited use when the relation of a focal predictor and an outcome is affected by a moderator. This study presents a conditional direction dependence analysis (CDDA) framework which enables researchers to evaluate the causal direction of conditional regression effects. Monte-Carlo simulations were used to evaluate two different moderation scenarios: Study 1 evaluates the performance of CDDA tests when a moderator affects the strength of the causal effect x → y. Study 2 evaluates cases in which the causal direction itself (x → y vs y → x) depends on moderator values. Study 3 evaluates the robustness of DDA tests in the presence of functional model misspecifications. Results suggest that significance tests compatible with CDDA are suitable in both moderation scenarios, i.e., CDDA allows one to discern regions of a moderator in which the causal direction is uniquely identifiable. An empirical example is provided to illustrate the approach.

Sensitivity Analysis and Extensions of Testing the Causal Direction of Dependence: A Rejoinder to Thoemmes (2019)

A commentary by Thoemmes on Wiedermann and Sebastian's introductory article on Direction Dependence Analysis (DDA) is responded to in the interest of elaborating and extending direction dependence principles to evaluate causal effect directionality. Considering Thoemmes' observation that some DDA principles are already well-established in machine learning, we argue that several other connections between DDA and research lines in theoretical statistics, econometrics, and quantitative psychology exist, suggesting that DDA is best conceptualized as a framework that summarizes and extends existing knowledge on properties of linear models under non-normality. Further, Thoemmes articulates concerns about assumptions of error distributions used in our main article and presents an artificial data example in which some DDA tests have suboptimal statistical power. We present extensions of DDA to entirely relax distributional assumptions about errors and describe two sensitivity analysis approaches to critically evaluate DDA results. Both sensitivity approaches are illustrated using Thoemmes' artificial data example. Incorporating DDA sensitivity results yields correct causal conclusions. Thus, overall, we stay with our initial conclusion that the use of higher moments in causal inference constitutes an exciting open research area.

Advances in Statistical Methods for Causal Inference in Prevention Science: Introduction to the Special Section

The board of the Society for Prevention Research noted recently that extant methods for the analysis of causality mechanisms in prevention may still be too rudimentary for detailed and sophisticated analysis of causality hypotheses. This Special Section aims to fill some of the current voids, in particular in the domain of statistical methods of the analysis of causal inference. In the first article, Bray et al. propose a novel methodological approach in which they link propensity score techniques and Latent Class Analysis. In the second article, Kelcey et al. discuss power analysis tools for the study of causal mediation effects in cluster-randomized interventions. Wiedermann et al. present, in the third article, methods of Direction Dependence Analysis for the identification of confounders and for inference concerning the direction of causal effects in mediation models. A more general approach to the identification of causal structures in non-experimental data is presented by Shimizu in the fourth article. This approach is based on linear non-Gaussian acyclic models. Molenaar introduces vector-autoregressive methods for the optimal representation of Granger causality in time-dependent data. The Special Section concludes with a commentary by Musci and Stuart. In this commentary, the contributions of the articles in the Special Section are highlighted from the perspective of the experimental causal research tradition.