We develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies.
<coherence> We develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies.
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.