Nonparametric estimation of average treatment effects under exogeneity: A review
Nonparametric estimation of average treatment effects under exogeneity: A review
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DOI：
10.1162/003465304323023651
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发表时间：
2004-02-01
影响因子：
8
通讯作者：
Imbens, GW
中科院分区：
文献类型：
--
作者：
Imbens, GW
最近，计量经济学的工作激增，重点是在各种假设下估计平均治疗效果。这些文献中的一部分已经开发出了估计平均治疗效果的方法，用于在各种描述为外源性、不混淆性或可观察量选择的假设下进行二元治疗。这些假设的含义是，具有相同协变量值的治疗单位和对照单位之间结局的系统性（例如，平均或分布）差异可归因于治疗。最近的分析认为，通过避免分布和函数形式假设，在比早期文献典型假设更弱的假设下，对平均治疗效果进行估计和推断。已经提出了各种半参数估计方法，包括估计未知的回归函数，匹配，使用倾向评分的方法，如加权和分块，以及这些方法的组合。在本文中，我回顾了这一文献的状态，并讨论了一些尚未回答的问题，特别是集中在这些方法的实际实施，这种外生性假设在经济应用中的可接受性，各种半参数估计的相对性能时，关键假设（不混淆和重叠），替代被估量，如分位数治疗效果，和替代方法，如贝叶斯推断。
Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional-form assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this literature and discuss some of its unanswered questions, focusing in particular on the practical implementation of these methods, the plausibility of this exogeneity assumption in economic applications, the relative performance of the various semiparametric estimators when the key assumptions (unconfoundedness and overlap) are satisfied, alternative estimands such as quantile treatment effects, and alternate methods such as Bayesian inference.