Biostatistics Seminar Series – Mihai Giurcanu, Ph.D.

Mihai Giurcanu, Ph.D.
Research Associate Professor, Biostatistics Laboratory & Research Computing Group, Department of Public Health Sciences, University of Chicago

“Causal Inference using Generalized Empirical Likelihood”

Abstract:
We propose a generalized empirical likelihood (GEL) method for causal inference based on a moment condition model that balances the treatment groups with respect to the potential confounders (e.g., as indicated by a directed acyclic graph). Allowing the number of moment conditions grow with the sample size, we show the asymptotic normality of the causal effects estimates without relying on regression models for the counterfactuals nor for the propensity scores. In a simulation study, we assess the finite sample properties of the proposed estimators in terms of the mean squared error and coverage of confidence intervals. We illustrate an application of the proposed method to data analysis of a training program study.
Authors: Pierre Chausse, Mihai Giurcanu and George Luta.

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Bio3 Seminar Series sponsored by Department of Biostatistics, Bioinformatics & Biomathematics (DBBB)