Quasi-oracle estimation of heterogeneous treatment effects
Quasi-oracle estimation of heterogeneous treatment effects
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DOI：
10.1093/biomet/asaa076
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发表时间：
2021-06-01
期刊：
影响因子：
2.7
通讯作者：
Wager, S.
中科院分区：
文献类型：
--
作者：
Nie, X.;Wager, S.
对异质治疗效果的灵活估计是许多统计应用的核心，例如个性化药物和最优资源分配。在这篇文章中，我们开发了一类一般的两步算法，用于观察性研究中的异质治疗效果估计。首先，我们估计边际效应和处理倾向，以形成分离信号的因果分量的目标函数。然后，我们对这个数据自适应目标函数进行了优化。与现有的方法相比，提出的方法有几个优点。从实践的角度来看，我们的方法灵活且易于使用：在这两个步骤中，都可以使用任何损失最小化方法，如惩罚回归、深度神经网络或Boost；而且，这些方法可以通过交叉验证进行微调。同时，在惩罚核回归的情况下，我们证明了我们的方法具有拟预言性。即使试验对边际效应和治疗倾向的估计不是特别准确，我们也达到了与先知对这两种滋扰成分的先验知识相同的误差界。我们在各种模拟设置中实现了基于惩罚回归、内核岭回归和增强的方法的变体，并观察到相对于现有基线的有前景的性能。
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical applications, such as personalized medicine and optimal resource allocation. In this article we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. First, we estimate marginal effects and treatment propensities to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. The proposed approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: in both steps, any loss-minimization method can be employed, such as 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 when the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle with prior knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation set-ups, and observe promising performance relative to existing baselines.