# Learning Triggers for Heterogeneous Treatment Effects

arXiv: https://arxiv.org/abs/1902.00087 · HTML: https://mlsanity.com/paper/1902.00087/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Heterogeneous Peer Effects in the Linear Threshold Model](https://mlsanity.com/paper/2201.11242/) (2022)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty](https://mlsanity.com/paper/2206.12689/) (2022)
- [Data-Driven Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/2301.06615/) (2023)
- [De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm](https://mlsanity.com/paper/2603.02174/) (2026)
- [Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items](https://mlsanity.com/paper/2106.11389/) (2021)

## Abstract (arXiv)

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.