Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding

TL;DR

This paper introduces a simple, assumption-free sensitivity analysis method for unmeasured confounding that provides sharp bounds on causal effects, requiring only two intuitive parameters, and extends to mediators and exposure-outcome confounding.

Contribution

The paper proposes a new, assumption-free sensitivity analysis method with sharp bounds requiring only two parameters, improving over previous methods and extending to mediators.

Findings

Bounds can be tighter than previous methods

Method is assumption-free and easy to implement

Extends to natural direct and indirect effects

Abstract

We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption-free. The method returns an interval that contains the true causal effect, and whose bounds are arbitrarily sharp, i.e. practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele (2016a) which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure-outcome confounding.