Bayesian causal inference offers a principled approach to policy evaluation of proposed interventions on mediators or time-varying exposures. Building on the Bayesian g-formula method introduced by Keil et al., we outline a general approach for the estimation of population-level causal quantities involving dynamic and stochastic treatment regimes, including regimes related to mediation estimands such as natural direct and indirect effects. We further extend this approach to propose a Bayesian data fusion (BDF), an algorithm for performing probabilistic sensitivity analysis when a confounder unmeasured in a primary data set is available in an external data source. When the relevant relationships are causally transportable between the two source populations, BDF corrects confounding bias and supports causal inference and decision-making within the main study population without sharing of the individual-level external data set. We present results from a simulation study comparing BDF to two common frequentist correction methods for unmeasured mediator-outcome confounding bias in the mediation setting. We use these methods to analyze data on the role of stage at cancer diagnosis in contributing to Black–White colorectal cancer survival disparities.

中文翻译：

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贝叶斯数据融合：使用基于二手数据的先验信息对未测量混杂进行概率敏感性分析

贝叶斯因果推理提供了一种原则性方法，用于对中介或时变风险的拟议干预措施进行政策评估。基于 Keil等人介绍的贝叶斯 g 公式方法., 我们概述了一种估计人口水平因果量的一般方法，涉及动态和随机治疗制度，包括与自然直接和间接影响等调解估计相关的制度。我们进一步扩展这种方法以提出贝叶斯数据融合 (BDF)，这是一种用于在外部数据源中提供原始数据集中未测量的混杂因素时执行概率敏感性分析的算法。当相关关系在两个源人群之间具有因果关系时，BDF 可以纠正混杂偏差并支持主要研究人群中的因果推理和决策制定，而无需共享个人层面的外部数据集。我们展示了一项模拟研究的结果，该研究将 BDF 与两种常见的常客校正方法进行了比较，以解决调解环境中未测量的调解结果混杂偏差。我们使用这些方法来分析癌症诊断分期在导致黑白结直肠癌生存差异方面的作用的数据。