Heterogeneous Treatment Effect-based Random Forest: HTERF

被引：0

作者：

Jocteur, Berenice-Alexia ^{[1
,2
]}

Maume-Deschamps, Veronique ^{[1
]}

Ribereau, Pierre ^{[1
]}

机构：

[1] Univ Jean Monnet, Univ Claude Bernard Lyon 1, Ecole Cent Lyon, ICJ UMR5208,CNRS,INSA Lyon, F-69622 Villeurbanne, France

[2] Natixis, Enterprise Risk Management, F-75013 Paris, France

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2024年 / 196卷

关键词：

Causal forest; Causal inference; Heterogeneous treatment effect; Potential outcomes;

D O I：

10.1016/j.csda.2024.107970

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：21

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