基于异质性治疗效果的随机森林：HTERF

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational Statistics & Data Analysis Pub Date : 2024-04-16 DOI:10.1016/j.csda.2024.107970

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

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引用次数: 0

摘要

需要对因果效应进行估计，以回答有关政策转变的假设问题，如药理学的新疗法或企业主的新定价策略。本文提出了一种新的非参数方法来估计基于随机森林（HTERF）的异质性治疗效果。无边界的潜在结果框架表明，HTERF 在点上几乎肯定与真实治疗效果一致。同时还给出了可解释性结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Heterogeneous Treatment Effect-based Random Forest: HTERF

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.

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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]

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