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Friday, January 12
Fri, Jan 12, 10:30 AM - 12:15 PM
Crystal Ballroom B
Instrumental Variables and Treatment Effect Heterogeneity

Model Selection for Estimating Treatment Effects (304213)

*Craig Anthony Rolling, Saint Louis University 
Yuhong Yang, University of Minnesota 

Keywords: causal inference, cross-validation, model selection, treatment effect heterogeneity

Estimation of (potentially) heterogeneous treatment effects is a popular area of research, and several methods have been recently proposed to estimate the effect of a treatment conditional on covariates. A data analyst who wants to estimate such conditional treatment effects must answer the question, “Which estimation procedure is best for the data I have?” Model selection methods typically focus on estimation of the regression function; however, within a set of candidate models, those that best estimate the regression function may not be the best for estimating the conditional effect of a treatment. The usual model selection methods may therefore be unsuitable for the purpose of treatment effect estimation. In this talk, we propose a modification of cross-validation that can be used for model selection targeted to estimating (potentially) heterogeneous treatment effects. Areas of future research also will be discussed.