## Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score (2000)

Citations: | 166 - 15 self |

### BibTeX

@MISC{Hirano00efficientestimation,

author = {Keisuke Hirano and Guido W. Imbens and Keisuke Hirano Ucla and Guido W. Imbens Ucla and Geert Ridder and We Thank Gary Chamberlain and Jinyong Hahn and Donald Rubin and Seminar Participants},

title = {Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score},

year = {2000}

}

### Years of Citing Articles

### OpenURL

### Abstract

We are interested in estimating the average e#ect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for di#erences in the pre-treatmentvariables. Rosenbaum and Rubin #1983, 1984# show that adjusting solely for di#erences between treated and control units in a scalar function of the pre-treatment variables, the propensity score, also removes the entire bias associated with di#erences in pre-treatment variables. Thus it is possible to obtain unbiased estimates of the treatment e#ect without conditioning on a possibly highdimensional vector of pre-treatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of e#ciency. We show that weighting with the inverse of a nonparametric estimate of the propensity score, rather than the true propensity scor...