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Endogenous treatment effect for any response conditional on control propensity score

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  • Choi, Jin-young
  • Lee, Goeun
  • Lee, Myoung-jae

Abstract

In finding X-heterogeneous effects of an endogenous treatment D on a response Y where X is covariates and an instrument δ is available, we overcome the X-dimension problem, by conditioning on the “control propensity score” E(D|X,δ=0). We propose two estimators allowing for any form of Y.

Suggested Citation

  • Choi, Jin-young & Lee, Goeun & Lee, Myoung-jae, 2023. "Endogenous treatment effect for any response conditional on control propensity score," Statistics & Probability Letters, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:stapro:v:196:y:2023:i:c:s0167715222002607
    DOI: 10.1016/j.spl.2022.109747
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Myoung-Jae Lee, 2018. "Simple least squares estimator for treatment effects using propensity score residuals," Biometrika, Biometrika Trust, vol. 105(1), pages 149-164.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    5. Linbo Wang & Eric Tchetgen Tchetgen, 2018. "Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 531-550, June.
    6. Lee, Myoung-jae, 2016. "Matching, Regression Discontinuity, Difference in Differences, and Beyond," OUP Catalogue, Oxford University Press, number 9780190258740.
    7. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    8. Myoung‐jae Lee, 2021. "Instrument residual estimator for any response variable with endogenous binary treatment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 612-635, July.
    9. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699.
    10. Elizabeth L. Ogburn & Andrea Rotnitzky & James M. Robins, 2015. "Doubly robust estimation of the local average treatment effect curve," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 373-396, March.
    11. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
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    Cited by:

    1. Lee Goeun & Choi Jin-young & Lee Myoung-jae, 2023. "Minimally capturing heterogeneous complier effect of endogenous treatment for any outcome variable," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-25.

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