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A General Double Robustness Result for Estimating Average Treatment Effects

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  • Sloczynski, Tymon

    () (Brandeis University)

  • Wooldridge, Jeffrey M.

    () (Michigan State University)

Abstract

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

Suggested Citation

  • Sloczynski, Tymon & Wooldridge, Jeffrey M., 2014. "A General Double Robustness Result for Estimating Average Treatment Effects," IZA Discussion Papers 8084, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp8084
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    References listed on IDEAS

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    1. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    2. Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, March.
    4. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    5. Rothe, Christoph & Firpo, Sergio Pinheiro, 2013. "Semiparametric estimation and inference using doubly robust moment conditions," Textos para discussão 330, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-537, May.
    7. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    8. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    9. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
    10. Boris Kaiser, 2013. "Decomposing Differences in Arithmetic Means: A Doubly-Robust Estimation Approach," Diskussionsschriften dp1308, Universitaet Bern, Departement Volkswirtschaft.
    11. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    12. Boris Kaiser, 2016. "Decomposing differences in arithmetic means: a doubly robust estimation approach," Empirical Economics, Springer, vol. 50(3), pages 873-899, May.
    13. Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.
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    Cited by:

    1. Hartley, Robert Paul & Lamarche, Carlos, 2018. "Behavioral responses and welfare reform: Evidence from a randomized experiment," Labour Economics, Elsevier, vol. 54(C), pages 135-151.
    2. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    3. Jörg Kalbfuß & Reto Odermatt & Alois Stutzer, 2018. "Medical Marijuana Laws and Mental Health in the United States," CEP Discussion Papers dp1546, Centre for Economic Performance, LSE.
    4. repec:eee:ecosys:v:41:y:2017:i:2:p:248-265 is not listed on IDEAS

    More about this item

    Keywords

    double robustness; inverse-probability weighting (IPW); multi-valued treatments; quasi-maximum likelihood estimation (QMLE); treatment effects;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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