Please join the Statistical Computing Workshop as they host Jake Bowers, Professor in the Departments of Political Science and Statistics at the University of Illinois at Urbana-Champaign, for a presentation titled "Two fully specified Bayes factors for hypothesis testing and sensitivity analysis in process tracing".
Abstract
Fairfield and Charman (2022) propose using a Bayes factor to summarize process tracing evidence, but they require researchers to specify the probability of evidence by hand, and this has drawn concern about bias (Zaks 2021). We present a solution by deriving such probabilities directly from two fully specified generative models of observation tailored to process-tracing research designs. Our fully specified Bayes factors enable researchers to report how much observation bias a positive conclusion can absorb before flipping in favor of the rival, taking dependence on smoking gun weight into consideration as well. In practice, this means that final conclusions are driven by sensitivity tests more than by Bayes factors themselves. To show the usefulness of our approach we apply the framework to six recent process-tracing studies published in top political science journals.
Bio
Jake Bowers is Proffesor in the Departments of Political Science and Statistics at the University of Illinois Urbana-Champaign. His methodological research develops computationally intensive, design-based approaches to the analysis of both randomized experiments and non-randomized observational studies in the service of understanding human behavior and improving public policy. The Society for Political Methodology elected him a Fellow in 2025. He currently serves as the Methods Director for the Evidence in Governance and Politics (EGAP) network and co-founder and co-director of the new Arnold Ventures Criminal Justice Research network.
Ariel Sowers
(847) 491-7454
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