论文标题
识别和自动贬值的机器学习，用于结果的平均结构衍生物
Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives
论文作者
论文摘要
本文提出了一类新的异质因果量，称为\ textit {结果条件}平均结构衍生物（OASD）在一般不可分离的模型中。 OASD是连续治疗中边缘变化的平均部分影响对位于结果分布不同部分的个体的平均部分影响，而与个人的特征无关。 OASD结合了ATE和QTE的两种特征：它的解释与Ate一样直接解释，同时通过根据结果分布的等级打破整个种群而比Ate更颗粒。 本文的一项贡献是，我们在\ textit {结果条件平均局部效应}与一类参数之间建立了一些密切的关系，这些参数衡量了对反合更改单个协变量分布对无条件结果分位数的分布的效果。通过利用这种关系，我们可以获得根 - $ n $一致的估计器，并计算这些反事实效应参数绑定的半参数效率。我们通过两个示例来说明这一点：OASD与无条件的部分分位数效应之间的等效性（Firpo等（2009）），以及边缘部分分布策略效应（Rothe（2012））与相应结果条件条件的参数之间的等效性。 由于通过控制有关协变量的丰富信息来实现OASD的识别，因此研究人员可以理想地在数据中使用高维控制。我们为OASD提出了一种新型的自动辩护机器学习估计器，并为其提供渐近统计保证。我们证明我们的估计器是root-$ n $一致的，渐近正常和半呈效率。我们还证明了自举程序对欧asd过程均匀推断的有效性。
This paper proposes a new class of heterogeneous causal quantities, named \textit{outcome conditioned} average structural derivatives (OASD) in a general nonseparable model. OASD is the average partial effect of a marginal change in a continuous treatment on the individuals located at different parts of the outcome distribution, irrespective of individuals' characteristics. OASD combines both features of ATE and QTE: it is interpreted as straightforwardly as ATE while at the same time more granular than ATE by breaking the entire population up according to the rank of the outcome distribution. One contribution of this paper is that we establish some close relationships between the \textit{outcome conditioned average partial effects} and a class of parameters measuring the effect of counterfactually changing the distribution of a single covariate on the unconditional outcome quantiles. By exploiting such relationship, we can obtain root-$n$ consistent estimator and calculate the semi-parametric efficiency bound for these counterfactual effect parameters. We illustrate this point by two examples: equivalence between OASD and the unconditional partial quantile effect (Firpo et al. (2009)), and equivalence between the marginal partial distribution policy effect (Rothe (2012)) and a corresponding outcome conditioned parameter. Because identification of OASD is attained under a conditional exogeneity assumption, by controlling for a rich information about covariates, a researcher may ideally use high-dimensional controls in data. We propose for OASD a novel automatic debiased machine learning estimator, and present asymptotic statistical guarantees for it. We prove our estimator is root-$n$ consistent, asymptotically normal, and semiparametrically efficient. We also prove the validity of the bootstrap procedure for uniform inference on the OASD process.
