In causal inference, sensitivity analysis is important to assess the robustness of study conclusions to key assumptions. We perform sensitivity analysis of the assumption that missing outcomes are missing completely at random. We follow a Bayesian approach, which is nonparametric for the outcome distribution and can be combined with an informative prior on the sensitivity parameter. We give insight in the posterior and provide theoretical guarantees in the form of Bernstein-von Mises theorems for estimating the mean outcome. We study different parametrisations of the model involving Dirichlet process priors on the distribution of the outcome and on the distribution of the outcome conditional on the subject being treated. We show that these parametrisations incorporate a prior on the sensitivity parameter in different ways and discuss the relative merits. We also present a simulation study, showing the performance of the methods in finite sample scenarios.

翻译：在因果推断中，敏感性分析对于评估研究结论对关键假设的稳健性至关重要。我们对缺失结果完全随机缺失这一假设进行敏感性分析。我们采用贝叶斯方法，该方法对于结果分布是非参数的，并可结合关于敏感性参数的信息性先验。我们给出了后验的见解，并以伯恩斯坦-冯·米塞斯定理的形式为均值结果的估计提供了理论保证。我们研究了模型的不同参数化方法，包括对结果分布以及接受处理个体条件结果分布采用狄利克雷过程先验。我们表明这些参数化方法以不同方式纳入了关于敏感性参数的先验，并讨论了各自的优缺点。我们还进行了一项模拟研究，展示了这些方法在有限样本场景下的性能。