Title: Quasi-Oracle Estimation of Heterogeneous Treatment Effects,
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 neutral 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 both penalized
regression and boosting in a variety of simulation setups, and find promising
performance relative to existing baselines.