We propose an approach to better inform treatment decisions at an individual level by adapting recent advances in average treatment effect estimation to conditional average treatment effect estimation. Our work is based on doubly robust estimation methods, which combine flexible machine learning tools to produce efficient effect estimates while relaxing parametric assumptions about the data generating process. Refinements to doubly robust methods have achieved faster convergence by incorporating 3-way cross-fitting, which entails dividing the sample into three partitions, using the first to estimate the conditional probability of treatment, the second to estimate the conditional expectation of the outcome, and the third to perform a first order bias correction step. Here, we combine the approaches of 3-way cross-fitting and pseudo-outcome regression to produce personalized effect estimates. We show that this approach yields fast convergence rates under a smoothness condition on the conditional expectation of the outcome.

翻译：我们提出一种方法，通过将平均处理效应估计的最新进展适配到条件平均处理效应估计中，从而更好地为个体层面的治疗决策提供依据。我们的工作基于双重稳健估计方法，该方法结合了灵活的机器学习工具，在放松对数据生成过程的参数假设的同时产生有效的效应估计。对双重稳健方法的改进通过引入三种交叉拟合（将样本分为三部分：第一部分用于估计处理的条件概率，第二部分用于估计结果的条件期望，第三部分用于执行一阶偏差校正步骤）实现了更快的收敛速度。本文结合三种交叉拟合与伪结果回归方法，生成个性化的效应估计。我们证明，在结果条件期望满足光滑性条件的假设下，该方法能实现快速的收敛速率。