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). In this paper, 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.

翻译：Fairfield 与 Charman (2022) 提出使用贝叶斯因子来总结过程追踪证据，但该方法要求研究人员手动指定证据概率，引发了关于偏差的担忧 (Zaks 2021)。本文通过直接从两个专为过程追踪研究设计而完全指定的观测生成模型中推导此类概率，提出了解决方案。我们的完全指定贝叶斯因子使研究人员能够报告：在正向结论转向支持竞争假设前，可承受多少观测偏差（同时考虑"确凿证据"的权重依赖性）。实际应用中，这意味着最终结论更多地由敏感性检验而非贝叶斯因子本身驱动。为展示该方法的价值，我们将该框架应用于近期发表在顶级政治学期刊上的六项过程追踪研究。