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Model selection for estimating treatment effects
Author(s) -
Rolling Craig A.,
Yang Yuhong
Publication year - 2014
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12043
Subject(s) - estimator , treatment effect , covariate , selection (genetic algorithm) , flexibility (engineering) , computer science , regression , model selection , consistency (knowledge bases) , estimation , feature selection , statistics , data mining , machine learning , mathematics , medicine , artificial intelligence , engineering , systems engineering , traditional medicine
Summary Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, there is a need for model selection methods that are targeted to treatment effect estimation. We demonstrate an application of the focused information criterion in this setting and develop a treatment effect cross‐validation aimed at minimizing treatment effect estimation errors. Theoretically, treatment effect cross‐validation has a model selection consistency property when the data splitting ratio is properly chosen. Practically, treatment effect cross‐validation has the flexibility to compare different types of models. We illustrate the methods by using simulation studies and data from a clinical trial comparing treatments of patients with human immunodeficiency virus.