# Targeting relative risk heterogeneity with causal forests

arXiv: https://arxiv.org/abs/2309.15793 · HTML: https://mlsanity.com/paper/2309.15793/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Estimating Treatment Effects with Causal Forests: An Application](https://mlsanity.com/paper/1902.07409/) (2019)
- [Model-based Recursive Partitioning for Subgroup Analyses](https://mlsanity.com/paper/1605.04078/) (2016)
- [Narrowing the Gap: Random Forests In Theory and In Practice](https://mlsanity.com/paper/1310.1415/) (2013)
- [Parameterizing and Simulating from Causal Models](https://mlsanity.com/paper/2109.03694/) (2021)
- [Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?](https://mlsanity.com/paper/2303.16008/) (2023)
- [Individual Treatment Effect Prediction for ALS Patients](https://mlsanity.com/paper/1604.08720/) (2016)
- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [Identification of vaccine effects when exposure status is unknown](https://mlsanity.com/paper/2111.11548/) (2021)

## Built on by (most cited first, 1 of 1)

- [Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects](https://mlsanity.com/paper/2605.26288/) (2026)

## Abstract (arXiv)

The identification of heterogeneous treatment effects (HTE) across subgroups is of significant interest in clinical trial analysis. Several state-of-the-art HTE estimation methods, including causal forests, apply recursive partitioning for non-parametric identification of relevant covariates and interactions. However, the partitioning criterion is typically based on differences in absolute risk. This can dilute statistical power by masking variation in the relative risk, which is often a more appropriate quantity of clinical interest. In this work, we propose and implement a methodology for modifying causal forests to target relative risk, using a novel node-splitting procedure based on exhaustive generalized linear model comparison. We present results from simulated data that suggest relative risk causal forests can capture otherwise undetected sources of heterogeneity. We implement our method on real-world trial data to explore HTEs for liraglutide in patients with type 2 diabetes.