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 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 average treatment effect on the treated units and studies with survival outcomes. We also develop an R package saci that implements our sensitivity analysis estimators.

翻译：基于观察性研究的因果推断常因未测量的混杂因素而受到影响，导致基于无混杂假设的估计量产生偏倚。敏感性分析用于评估因果结论如何随未测量混杂程度的不同而变化。现有的大多数敏感性分析方法仅适用于特定类型的估计或检验策略。本文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。该框架基于选择偏倚的经典参数化方法，即通过观测协变量条件下实际结果与反事实结果的比较来刻画偏倚。由于仅需对标准估计量进行简单修改，该方法在实际应用中颇具吸引力。此外，它可自然扩展到其他因果推断场景，包括处理组的平均处理效应及生存结局研究。我们还开发了实现敏感性分析估计量的R语言包saci。