American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Fall 2018 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



DOUBLE DEEP LEARNING FOR ADJUSTING
COMPLEX CONFOUNDING STRUCTURES IN OBSERVATIONAL DATA

by

Yu Shyr
Professor and Chair
Department of Biostatistics
Vanderbilt University

Host: Dr. Shing Lee


Abstract

Complex confounding structures are often embedded in observational data, including electronic medical record (EMR) data. A robust yet efficient double deep learning approach is proposed to adjust for the complex confounding structures in comparative effectiveness analysis of EMR data. Specifically, deep neural networks are employed to estimate the conditional expectations of the outcome and the treatment allocation given observed baseline covariates under a semiparametric framework. An improved estimation scheme is further developed to enhance the finite sample performance of the proposed method. Comprehensive numerical studies have shown superior performance of the proposed method, as compared with other existing methods, in terms of reduced bias and mean squared error of the treatment effect estimate.


Date: Thursday, October 25, 2018
Time: 11:45 A.M. - 12:45 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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