# Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials

arXiv: https://arxiv.org/abs/1709.04862 · HTML: https://mlsanity.com/paper/1709.04862/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [How to select predictive models for causal inference?](https://mlsanity.com/paper/2302.00370/) (2023)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling](https://mlsanity.com/paper/2505.01145/) (2025)
- [Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion](https://mlsanity.com/paper/2510.26444/) (2025)

## Abstract (arXiv)

Assessing heterogeneous treatment effects has become a growing interest in advancing precision medicine. Individualized treatment effects (ITE) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees (Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate (SSS) method, as an alternative to greedy search, to speed up tree construction. RFIT outperforms the traditional `separate regression' approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT can be obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.