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A Note on the Role of the Propensity Score for Estimating Average Treatment Effects

Author

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  • Markus Frolich

Abstract

Hahn [Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66:315-331] derived the semiparametric efficiency bounds for estimating the average treatment effect (ATE) and the average treatment effect on the treated (ATET). The variance of ATET depends on whether the propensity score is known or unknown. Hahn attributes this to “dimension reduction.” In this paper, an alternative explanation is given: Knowledge of the propensity score improves upon the estimation of the distribution of the confounding variables.

Suggested Citation

  • Markus Frolich, 2004. "A Note on the Role of the Propensity Score for Estimating Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 23(2), pages 167-174.
    • Handle: RePEc:taf:emetrv:v:23:y:2004:i:2:p:167-174
    DOI: 10.1081/ETC-120039608
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    Cited by:

    1. Ashimwe, Olive, 2016. "An Economic Analysis Of Impact Of Weather Index-Based Crop Insurance On Household Income In Huye District Of Rwanda," Research Theses 265675, Collaborative Masters Program in Agricultural and Applied Economics.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Graham, Bryan S. & Pinto, Cristine Campos de Xavier, 2022. "Semiparametrically efficient estimation of the average linear regression function," Journal of Econometrics, Elsevier, vol. 226(1), pages 115-138.
    4. Ashimwe, Olive, 2016. "An Economic Analysis Of Impact Of Weather Index-Based Crop Insurance On Household Income In Huye District Of Rwanda," Research Theses 276460, Collaborative Masters Program in Agricultural and Applied Economics.

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