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Article Dans Une Revue Computational Statistics and Data Analysis Année : 2024

Heterogeneous Treatment Effect based Random Forest: HTERF

Résumé

Estimates of causal impacts can be needed to answer what-if questions about shifts in policy, such as new treatments in pharmacology or new pricing strategies for a business owner. In this paper we propose a non-parametric approach to estimate heterogeneous treatment effect based on random forests: HTERF. In the potential outcome framework with unconfoundedness we show that HTERF is pointwise a.s.-consistent to the true treatment effect. An interpretability result is also presented. A software implementation, CausalForest for Julia is available on the general repository of Julia.
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Dates et versions

hal-04112079 , version 1 (31-05-2023)

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Bérénice-Alexia Jocteur, Véronique Maume-Deschamps, Pierre Ribereau. Heterogeneous Treatment Effect based Random Forest: HTERF. Computational Statistics and Data Analysis, 2024, 196, pp.107970. ⟨10.1016/j.csda.2024.107970⟩. ⟨hal-04112079⟩
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