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


  • Halbert White

    (University of California, San Diego)

  • Xun Lu

    (Hong Kong University of Science and Technology)


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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    References listed on IDEAS

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    5. Jakob Madsen, 2008. "Semi-endogenous versus Schumpeterian growth models: testing the knowledge production function using international data," Journal of Economic Growth, Springer, vol. 13(1), pages 1-26, March.
    6. Dan Ben-David, 1993. "Equalizing Exchange: Trade Liberalization and Income Convergence," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 653-679.
    7. Jakob B. Madsen, 2008. "Economic Growth, TFP Convergence and the World Export of Ideas: A Century of Evidence," Scandinavian Journal of Economics, Wiley Blackwell, vol. 110(1), pages 145-167, March.
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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. 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.
    4. Lu, Xun & White, Halbert, 2014. "Testing for separability in structural equations," Journal of Econometrics, Elsevier, vol. 182(1), pages 14-26.
    5. Deuchert, Eva & Huber, Martin, 2014. "A cautionary tale about control variables in IV estimation," Economics Working Paper Series 1439, University of St. Gallen, School of Economics and Political Science.
    6. 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.
    7. 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.
    8. 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.
    9. repec:bla:obuest:v:79:y:2017:i:3:p:411-425 is not listed on IDEAS
    10. 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.

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