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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.

Suggested Citation

  • Halbert White & Xun Lu, 2011. "Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1453-1459, November.
    • Handle: RePEc:tpr:restat:v:93:y:2011:i:4:p:1453-1459
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    Citations

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    Cited by:

    1. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
    2. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.
    3. Arno Parolini & Wei Wu Tan & Aron Shlonsky, 2019. "Decision-based models of the implementation of interventions in systems of healthcare: Implementation outcomes and intervention effectiveness in complex service environments," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-17, October.
    4. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    5. Lewbel, Arthur & Lu, Xun & Su, Liangjun, 2015. "Specification testing for transformation models with an application to generalized accelerated failure-time models," Journal of Econometrics, Elsevier, vol. 184(1), pages 81-96.
    6. Eva Deuchert & Martin Huber, 2017. "A Cautionary Tale About Control Variables in IV Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(3), pages 411-425, June.
    7. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    8. Pingel, Ronnie & Waernbaum, Ingeborg, 2015. "Correlation and efficiency of propensity score-based estimators for average causal effects," Working Paper Series 2015:3, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    9. Graevenitz, Georg von & Weber, Richard, 2011. "How to Educate Entrepreneurs?," Discussion Papers in Business Administration 12280, University of Munich, Munich School of Management.
    10. Ali Tafti & Galit Shmueli, 2020. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure," Information Systems Research, INFORMS, vol. 31(4), pages 1183-1199, December.
    11. Lu, Xun & White, Halbert, 2014. "Testing for separability in structural equations," Journal of Econometrics, Elsevier, vol. 182(1), pages 14-26.
    12. 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.
    13. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.

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