Econ Papers Daily / 期刊论文
密集边际与广延边际下的处理效应评估
Treatment evaluation at the intensive and extensive margins
摘要
This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to non-compliance. The existence of such a population is crucially tied to the regularity of sharp population bounds and thus conventional asymptotic inference for methods such as Lee bounds can be misleading. This problem can be solved using smoothed outer identification regions for inference. We provide semiparametrically efficient debiased machine learning estimators for both regular and smooth bounds that can accommodate high-dimensional covariates and flexible functional forms. Our study of active labor market policy reveals the empirical prevalence of the aforementioned indifference population and supports results from previous impact analyses under much weaker assumptions.
中文摘要
本文为在既无工具变量也无参数假设可用时，选择性样本中处理效应的评估提供了一种解决方案。我们在对所有主层（即刻画完整集约边际与广延边际的单元）施加条件单调性假设下，给出了平均处理效应的尖锐界。最重要的是，我们允许存在很大比例的单元，其选择对处理无差异，例如由于不依从所致。此类总体的存在与尖锐总体界的正则性密切相关，因此诸如Lee界之类方法的传统渐近推断可能产生误导。该问题可通过使用平滑的外识别区域进行推断来解决。我们为规则界和平滑界提供了半参数有效的去偏机器学习估计量，其能够容纳高维协变量和灵活的函数形式。我们对积极劳动力市场政策的研究揭示了上述无差异总体在经验上的普遍性，并在弱得多的假设下支持了以往影响分析的结果。