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A note on using the estimated versus the known propensity score to estimate the average treatment effect

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  • Brumback, Babette A.

Abstract

We provide simple intuition why using the estimated versus known propensity score tends to increase, and never decreases, asymptotic efficiency. When a covariate is independent of response conditional on treatment, using the known score can have greater finite-sample efficiency.

Suggested Citation

  • Brumback, Babette A., 2009. "A note on using the estimated versus the known propensity score to estimate the average treatment effect," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 537-542, February.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:4:p:537-542
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    References listed on IDEAS

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    1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    2. Joshua Angrist & Jinyong Hahn, 2004. "When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 58-72, February.
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    Cited by:

    1. Corwin Matthew Zigler, 2016. "The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 47-54, February.

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