Please join us for this week’s Biostatistics & Medical Informatics seminar on Friday!
Time: Friday, November 12 2021 at 12:00-1:00pm
Zoom Link: https://uwmadison.zoom.us/j/93605616843
Title: Semiparametric Sensitivity Analysis: Unmeasured Confounding in Observational Studies
Presenter: Dr. Daniel Scharfstein, Department of Population Health Sciences, University of Utah School of Medicine
Abstract: Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from nonexperimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We build on the work of Franks et al.  and Robins et al.  by specifying non-identified sensitivity parameters that govern a contrast between the conditional (on measured covariates) distributions of the outcome under treatment (control) between treated and untreated individuals. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step bias-corrected estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has √ n asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study. This is joint work with Razieh Nabi, Edward Kennedy, Ming-Yueh Huang, Matteo Bonvini and Marcela Smid.