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 estimation of RxC ecological inference contingency tables from aggregate data is one of the most salient and challenging problems in the field of quantitative social sciences, with major solutions proposed from both the ecological regression and the mathematical programming frameworks. In recent decades, there has been a drive to find…

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…

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…

Scholars and legal practitioners of voting rights are concerned with estimating individual-level voting behavior from aggregate-level data. The most commonly used technique, King's ecological inference (EI), has been questioned for inflexibility in multiethnic settings or with multiple candidates. One method for estimating vote support for…

Scholars who conduct process tracing often face the problem of missing data. The inability to document key steps in their causal chains makes it difficult to validate theoretical models. In this article, we conceptualize "missingness" as it relates to process tracing, describe different scenarios in which it is pervasive, and present…

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…

This article pushes forward a critical dialogue about the value of visualization as a method of sociological theorizing. Building on a nascent literature, I argue theory diagrams may operate not only conjunctively but also disjunctively, independent from empirics; that their theoretical value lies not only in capturing sociological problems but…

Advice on case selection in small-"N" research emphasizes controlling for confounding variables to facilitate inferential tests of a cross-case pattern. Yet many researchers embrace the "mechanismic worldview" and aim to construct explanations. Explanations differ from inferences because one explains an outcome at the…

To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations…

Recent years have witnessed a host of innovations for conducting research with qualitative comparative analysis (QCA). Concurrently, important issues surrounding its uses have been highlighted. In this article, we seek to help users design QCA studies. We argue that establishing inference with QCA involves three intertwined design components:…

The internal validity of observational study is often subject to debate. In this study, we define the counterfactuals as the unobserved sample and intend to quantify its relationship with the null hypothesis statistical testing (NHST). We propose the probability of a robust inference for internal validity, that is, the PIV, as a robustness index…

Agent-based modeling holds great potential as an analytical tool. Agent-based models (ABMs) are, however, also vulnerable to critique, as they often employ stylized social worlds, with little connection to the actual environment in question. Given these concerns, there has been a recent call to more fully incorporate empirical data into ABMs. This…

Theories of causation in philosophy ask what makes causal claims true and establish the so-called truth conditions allowing one to separate causal from noncausal relationships. We argue that social scientists should be aware of truth conditions of causal claims because they imply which method of causal inference can establish whether a specific…

Researchers interested in studying the frequency of events or behaviors among a population must rely on count data provided by sampled individuals. Often, this involves a decision between live event counting, such as a behavioral diary, and recalled aggregate counts. Diaries are generally more accurate, but their greater cost and respondent burden…