Causality in Policy Studies: a Pluralist Toolbox

This volume provides a methodological toolbox for conducting policy research. Recognizing that policy research spans various academic disciplines, each of which takes a different view on causality, the volume introduces a methodologically pluralistic approach to policy studies. Each chapter clarifies the research question that each technique can answer, the research design and data treatment that each technique requires for its results to be sound, the validity domain of its results, and the actual deployment of the technique through a replicable example. Techniques covered include quasi-experimental designs, approaches to account for selection bias and observed imbalances, directed acyclic graphs and structural equation models, Qualitative Comparative Analysis, Bayesian case study and process tracing, and Agent-Based Modelling. By working through the volume, readers will understand how to learn from different techniques, apply them consciously, and triangulate them to make better sense of findings. This volume is intended for advanced academic courses, as well as scholars and practitioners in policy-related fields, such as political science, economics, sociology, and public administration. This is an open access book.

1. Introduction: the Elephant of Causation and the Blind Sages -- 2. Causation in the Social Realm -- 3. Counterfactuals with Experimental and Quasi-Experimental Variation -- 4. Correlation is not Causation, yet… Matching and Weighting for Better Counterfactuals -- 5. Getting the Most Out of Surveys: Multilevel Regression And Poststratification -- 6. Pathway analysis, causal mediation and the identification of causal mechanisms -- 7. Proof by Testing Joint Sufficiency Twice: Explanatory Qualitative Comparative Analysis -- 8. Causal Inference and Policy Evaluation from Case Studies using Bayesian Process Tracing -- 9. Exploring Interventions ogn Social Outcomes with in Silico, Agent-Based Experiments -- 10. The Many Threats from Mechanistic Heterogeneity that Can Spoil Multi-Method Research.