Learning optimal biomarker-guided treatment policy for chronic disorders

DOI10.1002/sim.10099zbMATH Open1546.62861MaRDI QIDQ6615930

Qinxia Wang, Unnamed Author, Yuanjia Wang, Bin Yang, Ji Meng Loh

Publication date: 8 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

electroencephalographybiomarkerstreatment policyheterogeneous treatment effectmajor depressive disordercausal forests

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Generalized random forests
• Cross-Validation, Risk Estimation, and Model Selection: Comment on a Paper by Rosset and Tibshirani
• Root-N-Consistent Semiparametric Regression
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning
• A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
• Policy Learning With Observational Data
• Deep Neural Networks for Estimation and Inference
• Quasi-oracle estimation of heterogeneous treatment effects
• Double/debiased machine learning for treatment and structural parameters
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Random forests
• Locally Robust Semiparametric Estimation

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