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…

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…

In the article, we focused on the issues of measurement error and omitted confounders while conducting mediation analysis under experimental studies. Depending on informativeness of the confounders between the mediator (M) and outcome (Y), we described two approaches. When researchers are confident that primary confounders are included (e.g.,…

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…

Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…

Background: Studying causal effects is an important aim in education. Causal relationships indicate how well implements (e.g., interventions) work for the target subjects. A good strategy to get the inference in such relationships is to conduct randomized experiments. However, random assignment is limited in education research, even is discouraged…

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…

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…

Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…

Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…

Evaluators are often called upon to assess the effects of programs. To assess a program effect, evaluators need a clear understanding of how a program effect is defined. Arguably, the most widely used definition of a program effect is the counterfactual one. According to the counterfactual definition, a program effect is the difference between…

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. The local average treatment effects (LATE) framework for causal inference helped make this so. The LATE theorem tells us for whom particular instrumental variables (IV) and regression discontinuity estimates are valid. This lecture…

Complex social scientific theories are conventionally tested using linear structural equation modeling (SEM). However, the underlying assumptions of linear SEM often prove unrealistic, making the decomposition of direct and indirect effects problematic. Recent advancements in causal mediation analysis can help to address these shortcomings,…

In mixed methods approaches, statistical models are used to identify "nested" cases for intensive, small-n investigation for a range of purposes, including notably the examination of causal mechanisms. This article shows that under a commonsense interpretation of causal effects, large-n models allow no reliable conclusions about effect…

Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying…