Rethinking Causality: Explanation, Prediction, and Evidence in Political Science

Causal inference has transformed empirical political science. Experiments, natural experiments, instrumental variables, difference-in-differences designs, and related methods have substantially improved the discipline's ability to make credible claims about cause and effect. Yet this success has also generated a growing debate about what causal identification can - and cannot - tell us about the political and social world. This Research Topic encourages scholars from across political science and neighboring disciplines to reconsider the place of causality within empirical social science. Its starting point is neither a rejection nor a defense of causal inference, but a broader scientific question: what do we learn when we identify a causal effect, and what remains unexplained? This question opens several lines of inquiry. Estimated effects may depend on researcher decisions, model specifications, samples, measurements, and publication processes. Average treatment effects can obscure substantial heterogeneity, while results obtained in one population, institution, or historical setting may travel poorly to another. Experiments and observational designs require assumptions when researchers move from observed estimates to counterfactual scenarios, when they want to identity the mechanisms, and when they want to generalize the results to other contexts and data. More fundamentally, credible identification of a causal effect raises a further question: how does that finding contribute to the central social science goal of explaining the phenomenon under study?At the same time, developments in prediction, machine learning, computational social science, causal graphs, open science, meta-analysis, and large-scale evidence synthesis are expanding the methodological possibilities available to social scientists. These developments invite us to reconsider not only how causal claims are produced, but how different types of empirical evidence can contribute to cumulative knowledge and stronger theory. We therefore welcome theoretical, methodological, and empirical contributions addressing • causal identification and explanation; • counterfactual reasoning; • external validity and generalization; • treatment-effect heterogeneity; • researcher degrees of freedom and specification uncertainty; • replication and publication bias; • prediction and machine learning; • evidence synthesis and cumulative knowledge; • measurement and description; • and the relationship between empirical methods and substantive theory. The broader aim is constructive: to ask how causality, prediction, description, and theory can operate as complementary components of scientific explanation rather than competing methodological camps. In doing so, the Research Topic seeks to stimulate a wider conversation about what constitutes convincing evidence - and ultimately explanation - in contemporary political and social science. Topic editors: Matthew Loveless, Chiara Binelli Source: https://www.frontiersin.org/research-topics/86275/rethinking-causality-explanation-prediction-and-evidence-in-political-science