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Cited by 2,355

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

Hirano, Keisuke, Imbens, Guido W., Ridder, Geert·2003·Econometrica
DOI 10.1111/1468-0262.00442
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Abstract

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates. Although adjusting for differences in the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, and Todd (1998), and Robins, Mark, and Newey (1992). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to an efficient estimate of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.

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Metadata

Authors
Hirano, K., Imbens, G. W., & Ridder, G.
Venue
Econometrica
Publisher
The Econometric Society
Published
2003-07
Volume
71
Issue
4
Pages
1161-1189
ISSN
  • 0012-9682 (ISSN-L)
  • 1468-0262
DOI
10.1111/1468-0262.00442
OpenAlex
W3122193054
Cited by
2,355
References
4
FWCI
34.55
Type
journal article
Language
en
Open access
closed

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