论文标题
使用尺寸降低的有条件平均治疗效果的强大推断
Robust inference of conditional average treatment effects using dimension reduction
论文作者
论文摘要
重要的是要从观察数据对条件平均治疗效果进行强有力的推断，但是当混杂因素是多变量或高维时，这变得具有挑战性。在本文中，我们提出了一种双维方法，该方法在保持非参数优点的同时，尽可能地降低了维度的诅咒。我们使用降低尺寸确定条件平均治疗效应的中心平均子空间。具有先验维度降低的非参数回归也用于引起反事实结果。与现有方法相比，此步骤有助于提高插补的稳定性，并导致更好的估计器。然后，我们提出了一个有效的引导程序，而无需引导估计的中央平均值子空间以进行有效推理。
It is important to make robust inference of the conditional average treatment effect from observational data, but this becomes challenging when the confounder is multivariate or high-dimensional. In this article, we propose a double dimension reduction method, which reduces the curse of dimensionality as much as possible while keeping the nonparametric merit. We identify the central mean subspace of the conditional average treatment effect using dimension reduction. A nonparametric regression with prior dimension reduction is also used to impute counterfactual outcomes. This step helps improve the stability of the imputation and leads to a better estimator than existing methods. We then propose an effective bootstrapping procedure without bootstrapping the estimated central mean subspace to make valid inference.
