Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption that the measured covariates are enough to justify conditional exchangeability. We propose the P-learner, motivated by the R-learner, a tailored two-stage loss function for learning heterogeneous treatment effects in settings where exchangeability given observed covariates is an implausible assumption, and we wish to rely on proxy variables for causal inference. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods, which in the case of kernel regression satisfies an oracle bound on the estimated error as long as the nuisance components are estimated reasonably well.

翻译：高效且灵活地估计治疗效果异质性是医学到市场营销等众多领域中的重要任务，目前已有相当多具有前景的条件平均治疗效果估计器。然而，这些方法通常依赖一个假设：测量的协变量足以证明条件可交换性。受R-learner启发，我们提出P-learner，这是一种定制化的两阶段损失函数，用于在观测协变量下的可交换性假设不合理、且希望依赖代理变量进行因果推断的场景中学习异质性治疗效果。我们提出的估计器可通过现成的损失最小化机器学习方法实现，在核回归情形下，只要干扰成分估计得足够好，该估计器即满足估计误差的奥拉卡界。