Bayesian Inference with Multiple Cases: Unifying Process Tracing and Comparative Analysis (VIRTUAL)

Tasha Fairfield 
Associate Professor in Development Economics
London School of Economics

Drawing on Social Inquiry and Bayesian Inference: Rethinking Qualitative Research (CUP), this virtual talk will give a Bayesian perspective on within-case (process tracing) and cross-case (comparative) analysis.  Methodological literature often treats these as distinct analytical endeavors that draw on different logics of inference.  Within a Bayesian framework, however, there are no fundamental distinctions; all evidence contributes to inference in the same manner, whether we are studying a single case or multiple cases.  In essence, each piece of evidence weighs in favor of one explanation over a rival to some degree, which we assess by asking which explanation makes that evidence more expected. Evidentiary weight then aggregates both within any given case, and across different cases that fall within the scope of the theories we are testing. This Bayesian approach simplifies our understanding of inference, and more naturally mirrors the way that qualitative scholars learn from cumulative knowledge and generalize their findings.