Learning Triggers for Heterogeneous Treatment Effects
This addresses a problem in precision medicine and recommender systems by enabling personalized treatment prescriptions, but it appears incremental as it builds on existing heterogeneous treatment effect estimation methods.
The paper tackles the problem of heterogeneous treatment effect estimation by defining a variant where an individual-level threshold in treatment must be reached to trigger an effect, and proposes a tree-based learning method that can prescribe individualized treatments, with experimental results showing it learns triggers better than existing approaches.
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender systems. In this paper we define and study a variant of this problem in which an individual-level threshold in treatment needs to be reached, in order to trigger an effect. One of the main contributions of our work is that we do not only estimate heterogeneous treatment effects with fixed treatments but can also prescribe individualized treatments. We propose a tree-based learning method to find the heterogeneity in the treatment effects. Our experimental results on multiple datasets show that our approach can learn the triggers better than existing approaches.