Uncertainty undermines causal claims; however, the nature of causal claims decides what counts as relevant uncertainty. Empirical robustness is imperative in regularity theories of causality. Regularity theory features strongly in QCA, making its case sensitivity a weakness. Following qualitative comparative analysis (QCA) founder Charles Ragin's…

The field of qualitative comparative analysis (QCA) is witnessing a heated debate on which one of the QCA's main solution types should be at the center of substantive interpretation. This article argues that the different QCA solutions have complementary strengths. Therefore, researchers should interpret the three solution types in an integrated…

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…

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…

In the article "It's Just an Observation," Robinson and Wainer (Educational Psychology Review 35, Robinson, D., & Wainer, H. (2023). It's just an observation. Educational Psychology Review, 35(83), Published online: 14 August, 2023) lamented that educational psychology is moving toward the dark side of the quality continuum, with…

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of…

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the…

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…

Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and…

In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal…

Comparative and case study researchers have responded to critiques of their methods by developing formal procedures to validate theoretical claims through set theoretical logics of causal conditions. This 'logico-formalist turn' has involved the stricter application of the schemas of set theory and the philosophy of logic to raise validation…

Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…

Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal…

The consequence of a change in school leadership (e.g., principal turnover) on student achievement has important implications for education policy. The impact of such an event can be estimated via the popular Difference in Difference (DiD) estimator, where those schools with a turnover event are compared to a selected set of schools that did not…

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…