Causal inference with observational studies often suffers from unmeasured confounding, yielding biased estimators based on the unconfoundedness assumption. Sensitivity analysis assesses how the causal conclusions change with respect to different degrees of unmeasured confounding. Most existing sensitivity analysis methods work well for specific types of statistical estimation or testing strategies. We propose a flexible sensitivity analysis framework that can deal with commonly used inverse probability weighting, outcome regression, and doubly robust estimators simultaneously. It is based on the well-known parametrization of the selection bias as comparisons of the observed and counterfactual outcomes conditional on observed covariates. It is attractive for practical use because it only requires simple modifications of the standard estimators. Moreover, it naturally extends to many other causal inference settings, including the causal risk ratio or odds ratio, the average causal effect on the treated units, and studies with survival outcomes. We also develop an R package saci to implement our sensitivity analysis estimators.

翻译：基于观察性研究的因果推断常受未测量混杂影响，导致基于无混杂假设的估计量存在偏倚。敏感性分析用于评估因果结论随未测量混杂程度变化的情况。现有敏感性分析方法大多适用于特定类型的统计估计或检验策略。我们提出了一种灵活的敏感性分析框架，可同时处理常用的逆概率加权、结果回归及双重稳健估计量。该方法基于选择偏倚的一种经典参数化形式——即通过观察协变量条件下的观测结果与反事实结果比较来表示。其实际应用优势在于仅需对标准估计量进行简单调整。此外，该方法可自然扩展到多种因果推断场景，包括因果风险比或比值比、针对处理组的平均因果效应，以及生存结局研究。我们还开发了R语言包saci来实施该敏感性分析估计量。