Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest

TL;DR

This paper introduces Causal-DRF, a forest-based method for estimating the conditional kernel treatment effect, capturing distributional differences beyond mean effects using Distributional Random Forests.

Contribution

It adapts the DRF algorithm to estimate CKTE, providing a consistent, asymptotically normal estimator and enabling distributional treatment effect analysis.

Findings

Establishes a consistent estimator for CKTE.

Provides asymptotic normality and sampling distribution approximation.

Enables comprehensive distributional treatment effect analysis.

Abstract

The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE will fail to capture effects of treatments beyond differences in conditional expectations. Inspired by causal forests for CATE estimation, we develop a forest-based method to estimate the conditional kernel treatment effect (CKTE), based on the recently introduced Distributional Random Forest (DRF) algorithm. Adapting the splitting criterion of DRF, we show how one forest fit can be used to obtain a consistent and asymptotically normal estimator of the CKTE, as well as an approximation of its sampling distribution. This allows to study the difference in distribution between control and treatment group and thus yields a more comprehensive understanding of the treatment effect.