Efficient estimation of average treatment effects using the estimated propensity score
Efficient estimation of average treatment effects using the estimated propensity score
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DOI：
10.1111/1468-0262.00442
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发表时间：
2003-07-01
期刊：
影响因子：
6.1
通讯作者：
Ridder, G
中科院分区：
文献类型：
--
作者：
Hirano, K;Imbens, GW;Ridder, G
我们感兴趣的是估计二元治疗对标量结果的平均影响。如果分配给治疗的是外源性的或没有混淆的，即与给定的协变量的潜在结果无关，则可以通过调整协变量中的差异来消除与简单的治疗对照平均比较相关的偏差。Rosenbaum和Rubin(1983)表明，仅根据治疗单位和对照单位在倾向得分上的差异进行调整，可以消除与协变量差异有关的所有偏差。尽管对倾向性得分的差异进行调整可以消除所有的偏差，但这可能是以牺牲效率为代价的，如Hahn(1998)、Heckman、Ichiura和Todd(1998)以及Robins、Mark和Newey(1992)所示。我们表明，通过倾向分数的非参数估计的倒数来加权，而不是真正的倾向分数，导致了对平均治疗效果的有效估计。我们通过证明这个估计量可以被解释为一个经验似然估计量，它有效地结合了关于倾向得分的信息，从而为这一结果提供了直觉。
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.