American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Spring 2019 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



INSTRUMENTAL VARIABLE LEARNING
OF MARGINAL STRUCTURAL MODELS

by

Eric Tchetgen Tchetgen
Luddy Family President's Distinguished Professor
The Wharton School
Statistics Department
University of Pennsylvania

Host: Dr. Caleb Miles


Abstract

In a seminal paper, Robins (1998) introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. He established identification of MSM parameters under a sequential randomization assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We extend Robins' MSM theory by considering identification of MSM parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our identification conditions essentially require that no unobserved confounder predicts compliance type at each follow-up time. Under this assumption, we obtain a large class of semiparametric estimators that extends standard inverse-probability weighting (IPW) and includes multiply robust estimators, including a locally semiparametric efficient estimator. The approach provides a unified solution to IV inference from point exposure to time-varying exposure settings, including mean models with possibly nonlinear link functions, quantile MSMs and time to event models such as Cox MSMs. Finally, we briefly discuss recent robust IV methods that further allow for violation of the core IV identifying condition, the exclusion restriction assumption, without compromising inference.


Date: Thursday, April 11, 2019
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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