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信息先验对未测量的混杂进行概率敏感性分析


贝叶斯因果推理为对中介变量或时变风险的拟议干预措施的政策评估提供了一种原则性方法。基于 Keil等人引入的贝叶斯 g 公式方法，我们概述了一种估计人口水平因果量的通用方法，涉及动态和随机处理方案，包括与中介估计值（例如自然直接和间接效应）相关的方案。我们进一步扩展了这种方法，提出了贝叶斯数据融合（BDF），这是一种当外部数据源中存在主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群体之间可因果转移时，BDF 会纠正混杂偏差，并支持主要研究群体内的因果推理和决策，而无需共享个人层面的外部数据集。我们提出了一项模拟研究的结果，该研究将 BDF 与两种常见的频率校正方法进行比较，以解决调解环境中未测量的调解员结果混杂偏差。我们使用这些方法来分析癌症诊断分期对黑人与白人结直肠癌生存差异的影响的数据。