Heterogeneous Treatment Effect-based Random Forest: HTERF

Bérénice-Alexia Jocteur, Véronique Maume-Deschamps and Pierre Ribereau

Computational Statistics & Data Analysis, 2024, vol. 196, issue C

Abstract: Estimates of causal effects are needed to answer what-if questions about shifts in policy, such as new treatments in pharmacology or new pricing strategies for business owners. A new non-parametric approach is proposed to estimate the heterogeneous treatment effect based on random forests (HTERF). The potential outcome framework with unconfoundedness shows that the HTERF is pointwise almost surely consistent with the true treatment effect. Interpretability results are also presented.

Keywords: Causal forest; Causal inference; Heterogeneous treatment effect; Potential outcomes (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:196:y:2024:i:c:s0167947324000549

DOI: 10.1016/j.csda.2024.107970

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