论文标题
Wasserstein随机森林以及在异质治疗效应中的应用
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
论文作者
论文摘要
我们通过提出随机森林的自然变异来估计关键条件分布，从而在异质治疗效果的背景下对因果推论提供了新的见解。为了实现这一目标，我们根据经验措施之间的Wasserstein距离来重铸Breiman的原始分裂标准。该重新制定表明随机森林非常适合估计条件分布，并提供了算法向多元产出的自然扩展。遵循布雷曼的构建哲学，我们提出了分裂规则的一些变体，这些变体非常适合条件分布估计问题。建立了一些初步的理论联系以及各种数值实验，这表明我们的方法如何有助于在复杂情况下进行更透明的因果推断。
We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional distributions. To achieve this, we recast Breiman's original splitting criterion in terms of Wasserstein distances between empirical measures. This reformulation indicates that Random Forests are well adapted to estimate conditional distributions and provides a natural extension of the algorithm to multivariate outputs. Following the philosophy of Breiman's construction, we propose some variants of the splitting rule that are well-suited to the conditional distribution estimation problem. Some preliminary theoretical connections are established along with various numerical experiments, which show how our approach may help to conduct more transparent causal inference in complex situations.
