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

TL;DR

This paper introduces a flexible sensitivity analysis framework for causal inference in observational studies that accounts for unmeasured confounding across various estimators and settings, enhancing practical applicability.

Contribution

It proposes a unified, adaptable sensitivity analysis method compatible with multiple estimators and causal inference scenarios, with an accompanying R package for implementation.

Findings

Framework handles inverse probability weighting, outcome regression, doubly robust estimators

Extends to causal risk ratio, odds ratio, and survival outcomes

Provides a practical R package 'saci' for implementation

Abstract

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.