Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 545 - Machine Learning and Nonparametric Methods in Causal Inference
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309818
Title: Sensitivity Analysis via the Proportion of Unmeasured Confounding
Author(s): Matteo Bonvini* and Edward Kennedy
Companies: Carnegie Mellon University and Carnegie Mellon University
Keywords: observational study; optimization; partial identification; semiparametric theory

In observational studies, identification of ATEs is generally achieved by assuming "no unmeasured confounding," possibly after conditioning on enough covariates. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this paper, we take a novel approach whereby the sensitivity parameter is the proportion of unmeasured confounding. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study's robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program