# Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data

arXiv: https://arxiv.org/abs/2003.03042 · HTML: https://mlsanity.com/paper/2003.03042/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies](https://mlsanity.com/paper/2208.14329/) (2022)
- [Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation](https://mlsanity.com/paper/2408.15055/) (2024)

## Abstract (arXiv)

We propose Causal Interaction Trees for identifying subgroups of participants that have enhanced treatment effects using observational data. We extend the Classification and Regression Tree algorithm by using splitting criteria that focus on maximizing between-group treatment effect heterogeneity based on subgroup-specific treatment effect estimators to dictate decision-making in the algorithm. We derive properties of three subgroup-specific treatment effect estimators that account for the observational nature of the data -- inverse probability weighting, g-formula and doubly robust estimators. We study the performance of the proposed algorithms using simulations and implement the algorithms in an observational study that evaluates the effectiveness of right heart catheterization on critically ill patients.