Bayesian Process Tracing (QMMR 2)
Half Day, 1:30 PM – 5:00 PM
Palais des congrés de Montréal, 513B
This short course outlines the logic of Bayesian process tracing and provides students with practical advice, examples, and exercises to enable them to use this method in their work. It builds on Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, by Tasha Fairfield and Andrew Charman (Cambridge University Press, 2022).
The course does not require any prior training in process training, Bayesianism, probability theory, or logic. The only math skills that will be assumed are basic arithmetic. The course is designed to complement the APSA short course led by Andrew Bennett, Jeffrey T. Checkel, and Tasha Fairfield, but each course can also be usefully taken independently from the other.
The core idea that motivates the course is that the way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred—whether the emergence of democracy in South Africa, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of regime change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we continually update our beliefs about which hypothesis provides the best explanation—or we may introduce a new alternative that occurs to us along the way.
Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis—e.g., “mobilisation from below drove democratization in South Africa by altering economic elites’ regime preferences,” (Wood 2001), or “a lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled”—given our relevant prior knowledge and new information that we obtain during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research.
This course introduces basic principles of Bayesian reasoning with the goal of helping us leverage common-sense understandings of inference and improve intuition when conducting causal analysis with qualitative evidence. We begin by introducing the general logic of Bayesian inference, that is, how we can update our prior view about which explanation is more plausible when we learn new evidence about our cases. We explain the importance of developing mutually exclusive explanations and discuss how to formulate well-constructed hypotheses to compare. We then elaborate practical procedures for evaluating the inferential import of the evidence by assessing its likelihood under rival hypotheses and weighing the totality of evidence to update our prior views about which hypothesis provides the best explanation. We include multiple examples and exercises drawing on published case studies from comparative politics and international relations to show how this updating process works in practice with real-world qualitative evidence.