Semiparametric Causal Models and Estimating Equations

Gary Chan, PhD
Professor of Biostatistics, University of Washington

Much of the causal inference literature has focused on nonparametric identification and functional estimation, which might suggest to some that regression modeling has lost its importance. However, assumptions of effect homogeneity often arise naturally in practice. These assumptions typically impose semiparametric restrictions through conditional moment restrictions, making them amenable to the powerful toolkit of estimating equations. This framework encompasses a wide range of problems, and this talk will discuss some of my group's recent works in this area.

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