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Activity Number: 6 - Recent Advance of Nonparametric and Semiparametric Techniques with Complex Data Structure
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #326984 Presentation
Title: Semiparametric Theory for Causal Inference with Negative Controls
Author(s): Xu Shi* and Wang Miao and Eric Tchetgen Tchetgen
Companies: Harvard University and Peking University and The Wharton School at the University of Pennsylvania
Keywords: causal inference; negative control; semiparametric theory; unmeasured confounding

Negative controls have a long history in laboratory sciences and epidemiology to rule out non-causal explanations and to detect unmeasured confounding. A negative control outcome is a variable known not to be causally affected by the treatment, while a negative control exposure is a variable known not to causally affect the outcome of interest. Recently, sufficient conditions have been established for nonparametric identification of the average causal effect subject to unmeasured confounding leveraging a pair of negative control exposure-outcome variables. In this talk, we provide a general semiparametric framework for estimation and inference about the average treatment effect with double negative control adjustment for unmeasured confounding. In particular, we derive the semiparametric efficiency bound under a nonparametric model for the observed data distribution, and we propose multiply robust locally efficient estimators when nonparametric estimation may not be feasible. We assess the performance of our methods under model misspecification in extensive simulation studies. Finally, we illustrate our methods with an application to evaluate the effect of higher education on wage.

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

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