American Statistical Association
New York City
Metropolitan Area Chapter

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



Dr. Andrea Rotnitzky
Professor, Universidad Torcuato Di Tella
Adjunct Professor of Biostatistics, Harvard University

Host: Ian McKeague


Covariate adjustment is often used for estimation of population average causal effects (ATE). In recent years graphical rules have been derived for determining, from a causal diagram, all covariate adjustment sets. Restricting attention to causal linear models, a very recent article introduced two graphical criterions: one to compare the asymptotic variance of linear regression estimators that control for certain distinct adjustment sets and a second to identify the optimal adjustment set that provides the smallest asymptotic variance.

In this talk, I will show that the same graphical criterions can be used in arbitrary causal diagrams when the goal is to minimize the asymptotic variance of non-parametric estimators of ATE that ignore the causal diagram assumptions. Furthermore, I will provide a graphical criterion to determine the optimal adjustment set among the minimal adjustment sets. In addition, I will provide another graphical criterion for determining when a non-parametric estimator of ATE is as efficient as an efficient estimator that exploits the causal diagram assumptions. Finally, I will show that for estimating the effect of time dependent treatments in the presence of time dependent confounders, there exist diagrams with no optimal adjustment sets.

Date: Friday, October 11, 2019
Time: 11:30 A.M. - 12:30 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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