This course introduces a novel framework for qualitative and mixed methods research in the social sciences, grounded in formal tools from causal inference and Bayesian reasoning. It equips you to integrate evidence across diverse data sources and to link theory and empirics in a coherent, transparent, and analytically rigorous way. We begin with a review of foundational concepts in causal inference and Bayesian updating - a versatile framework for refining theories in light of new information. Building on these tools, we show how to generate and justify inferences using within-case evidence (e.g., process tracing), cross-case correlations, or a principled combination of both. We also explore how causal models can guide research design - helping you identify which cases to study, what data to collect, and how to combine methods strategically to address complex empirical questions.

This course is relevant to graduate students, postdocs, or other researchers in social sciences who are interested in integrating quantitative and qualitative methods and connecting theory to empirical research in a systematic and transparent way using advanced causal inference methods. You should be interested in qualitative reasoning and case-level inference but also open to using quantitative methods.