Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Xinkun Nie Stefan Wager
Abstract
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penaliz
中文速览
想在个性化医疗、资源分配等场景中准确估计"不同人对同一干预措施的效果差异"（异质性处理效应，CATE），现有机器学习方法往往需要针对每种算法单独改造，既费力又缺乏理论保障。该研究提出了一种名为 R-learner 的两步框架：第一步用任意预测方法估计结果均值和处理倾向，通过 Robinson 变换把数据"去混淆"，构造一个专门捕捉因果信号的损失函数；第二步直接用 LASSO、梯度提升、核岭回归等现成工具去最小化这个损失函数，并可通过交叉验证自动调参。理论上，只要第一步的辅助估计收敛速度达到 $o(n^{-1/4})$，第二步的误差界就与"事先知道所有干扰项"的神谕（oracle）方法相当，即具备准神谕性质。在多组模拟实验中，R-learner 的表现优于或持平于现有主流基准方法，而其最大价值在于将"去除混淆"与"拟合效应函数"两项任务彻底分离，使研究者能够直接复用任何成熟的机器学习工具来估计因果效应，而无需对算法内部进行专门改造。
原文 arXiv:1712.04912;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1712.04912v4