Learning Triggers for Heterogeneous Treatment Effects


  • Christopher Tran University of Illinois at Chicago
  • Elena Zheleva University of Illinois at Chicago




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 individuallevel 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.




How to Cite

Tran, C., & Zheleva, E. (2019). Learning Triggers for Heterogeneous Treatment Effects. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5183-5190. https://doi.org/10.1609/aaai.v33i01.33015183



AAAI Technical Track: Machine Learning