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

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  • Halbert White

    (University of California, San Diego)

  • Xun Lu

    (Hong Kong University of Science and Technology)

Abstract

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). © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 93 (2011)
Issue (Month): 4 (November)
Pages: 1453-1459

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Handle: RePEc:tpr:restat:v:93:y:2011:i:4:p:1453-1459

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Cited by:
  1. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
  2. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.

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