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Random Forests of Interaction Trees for Estimating Individualized
  Treatment Effects in Randomized Trials

Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials

14 September 2017
X. Su
A. T. Peña
Lei Liu
R. Levine
    CML
ArXiv (abs)PDFHTML

Papers citing "Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials"

4 / 4 papers shown
Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
Wenjie Chen
Li Zhuang
Ziying Luo
Yu Liu
Jiahao Wu
Shengcai Liu
96
0
0
30 Oct 2025
Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
David Svensson
Erik Hermansson
N. Nikolaou
Konstantinos Sechidis
Ilya Lipkovich
CML
256
0
0
02 May 2025
How to select predictive models for causal inference?
How to select predictive models for causal inference?
M. Doutreligne
Gaël Varoquaux
ELMCML
285
3
0
01 Feb 2023
Heterogeneous Treatment Effect Estimation for Observational Data using
  Model-based Forests
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based ForestsStatistical Methods in Medical Research (SMMR), 2022
Susanne Dandl
Andreas Bender
Torsten Hothorn
CML
177
10
0
06 Oct 2022
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