Estimating heterogeneous treatment effects for general responses

Heterogeneous treatment effect models allow us to compare treatments at subgroup levels and are becoming increasingly popular in applications such as personalized medicine, advertising, and education. Regardless of the type of responses (continuous, binary, count, survival), most causal estimands focus on the differences between the treatment and control conditional means. In this paper, we propose an alternative estimand, DINA-the DIfference in NAtural parameters-to quantify heterogeneous treatment effects motivated by exponential families and the Cox model. Despite the type of responses, DINA is both convenient and more practical for modeling the influence of covariates on the treatment effect. Additionally, we introduce a meta-algorithm for DINA, enabling practitioners to utilize powerful off-the-shelf machine learning tools for the estimation of nuisance functions. This meta-algorithm is also statistically robust to errors in the nuisance function estimation. We demonstrate the efficacy of our method in combination with various machine learning base-learners on both simulated and real datasets.