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 penalized
regression, kernel ridge regression, and boosting in a variety of simulation
setups, and find promising performance relative to existing baselines.

本文提出了一个针对观测研究中异质性处理效应估计的两阶段算法，其中，通过估计边际效应和治疗倾向来形成一个分离因果信号的客观函数，然后优化这个数据自适应的目标函数。我们的方法具有灵活性和易用性，并且针对不同的实际情况也有优势。