Primary Submission Category: Sensitivity Analysis
A Sensitivity Analysis for the Average Derivative Effect
Authors: Jeffrey Zhang,
Presenting Author: Jeffrey Zhang*
In observational studies, sensitivity analysis is an important tool that can help determine the robustness of a causal conclusion to a certain level of unmeasured confounding. At the same time, exposures that arise in observational studies are often continuous rather than binary or discrete. Sensitivity analysis approaches for continuous exposures have now been proposed for several causal estimands. In this article, we focus on the average derivative effect, a classical estimand from the econometrics literature. We obtain closed-form bounds for the average derivative effect under a sensitivity model that constrains the odds ratio (at any two dose levels) of the generalized propensity score. We propose flexible, efficient estimators for the bounds, as well as point-wise and uniform confidence intervals. We examine the finite sample performance of the methods through simulations and illustrate the methods on a study assessing the effect of parental income on educational attainment.