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Updating Bayesian(s): A Critical Evaluation of Bayesian Process Tracing

Published online by Cambridge University Press:  27 May 2020

Sherry Zaks*
Affiliation:
University of Southern California, Department of Political Science, 3518 Trousdale Pkwy, VKC 327, Los Angeles, CA90087, USA. Email: szaks@usc.edu
*

Abstract

Given the increasing quantity and impressive placement of work on Bayesian process tracing, this approach has quickly become a frontier of qualitative research methods. Moreover, it has dominated the process-tracing modules at the Institute for Qualitative and Multi-Method Research (IQMR) and the American Political Science Association (APSA) meetings for over five years, rendering its impact even greater. Proponents of qualitative Bayesianism make a series of strong claims about its contributions and scope of inferential validity. Four claims stand out: (1) it enables causal inference from iterative research, (2) the sequence in which we evaluate evidence is irrelevant to inference, (3) it enables scholars to fully engage rival explanations, and (4) it prevents ad hoc hypothesizing and confirmation bias. Notwithstanding the stakes of these claims and breadth of traction this method has received, no one has systematically evaluated the promises, trade-offs, and limitations that accompany Bayesian process tracing. This article evaluates the extent to which the method lives up to the mission. Despite offering a useful framework for conducting iterative research, the current state of the method introduces more bias than it corrects for on numerous dimensions. The article concludes with an examination of the opportunity costs of learning Bayesian process tracing and a set of recommendations about how to push the field forward.

Type
Articles
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Xun Pang

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