Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection

Halbert White; Xun Lu

doi:10.1162/rest_a_00153

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Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection

Halbert White¹; Xun Lu²

¹ University of California San Diego · ² Hong Kong University of Science and Technology

The Review of Economics and Statistics 2011

Careful examination of the structure determining treatment choice and outcomes, as advocated by Heckman (2008), is central to the design of treatment effect estimators and, in particular, proper choice of covariates. Here, we demonstrate how causal diagrams developed in the machine learning literature by Judea Pearl and his colleagues, but not so well known to economists, can play a key role in this examination by using these methods to give a detailed analysis of the choice of efficient covariates identified by Hahn (2004).

DOI: 10.1162/rest_a_00153
Volume: 93 (4)
Pages: 1453-1459
Language: en
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