# Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

## TL;DR

This paper introduces a novel application of random forest methods within the counterfactual framework to accurately estimate individual treatment effects in observational data, effectively addressing confounding and heterogeneity.

## Contribution

It demonstrates the effectiveness of counterfactual synthetic forests for estimating individual treatment effects, especially in complex heterogeneous and confounded settings.

## Key findings

- Counterfactual synthetic forests perform well in complex settings.
- Adaptive RF methods with out-of-sample estimation improve accuracy.
- Application reveals links between drug use and sexual risk.

## Abstract

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find accurate estimation of individual treatment effects is possible even in complex heterogeneous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, in order to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.