Department of Statistics, University of California, Berkeley, CA 94720;
Department of Statistics, University of California, Berkeley, CA 94720.
Proc Natl Acad Sci U S A. 2019 Mar 5;116(10):4156-4165. doi: 10.1073/pnas.1804597116. Epub 2019 Feb 15.
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms-such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks-to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.
人们越来越感兴趣的是在实验和观察研究中估计和分析异质处理效应。我们描述了一些元算法，这些算法可以利用机器学习和统计学中的任何监督学习或回归方法来估计条件平均处理效应 (CATE) 函数。元算法建立在基础算法之上，例如随机森林 (RFs)、贝叶斯加法回归树 (BARTs) 或神经网络，以估计 CATE，这是基础算法无法直接估计的函数。我们引入了一个元算法，即 X 学习者，当一个治疗组中的单位数量远远大于另一个治疗组时，它是可证明有效的，并且可以利用 CATE 函数的结构特性。例如，如果 CATE 函数是线性的，并且治疗和控制中的响应函数是 Lipschitz 连续的，那么在正则条件下，X 学习者仍然可以达到参数速率。然后，我们引入了使用 RF 和 BART 作为基础学习者的 X 学习者版本。在广泛的模拟研究中，X 学习者表现良好，尽管没有一种元算法始终是最好的。在两个来自政治学的说服现场实验中，我们展示了如何使用我们的 X 学习者来针对治疗方案，并揭示潜在的机制。我们提供了一个软件包来实现我们的方法。
