Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding

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.