Variable Selection in Causal Inference using a Simultaneous Penalization Method

Variable Selection in Causal Inference using a Simultaneous Penalization Method AbstractIn the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in estimation of the treatment effect. We propose a variable selection method using a penalized objective function that is based on both the outcome and treatment assignment models. The proposed method facilitates confounder selection in high-dimensional settings. We show that under some mild conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. Using the proposed method we analyze a set of data on economic growth and study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Causal Inference de Gruyter

Variable Selection in Causal Inference using a Simultaneous Penalization Method

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Publisher
de Gruyter
Copyright
© 2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2193-3685
eISSN
2193-3685
D.O.I.
10.1515/jci-2017-0010
Publisher site
See Article on Publisher Site

Abstract

AbstractIn the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in estimation of the treatment effect. We propose a variable selection method using a penalized objective function that is based on both the outcome and treatment assignment models. The proposed method facilitates confounder selection in high-dimensional settings. We show that under some mild conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. Using the proposed method we analyze a set of data on economic growth and study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita.

Journal

Journal of Causal Inferencede Gruyter

Published: Mar 26, 2018

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