A reluctant additive model framework for interpretable nonlinear individualized treatment rules
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Publication:6138648
DOI10.1214/23-aoas1767arXiv2311.01538OpenAlexW4388055290MaRDI QIDQ6138648
Jared D. Huling, Jacob M. Maronge, Guanhua Chen
Publication date: 16 January 2024
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2311.01538
Cites Work
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- Performance guarantees for individualized treatment rules
- Generalized random forests
- Component selection and smoothing in multivariate nonparametric regression
- High-dimensional additive modeling
- Concordance and value information criteria for optimal treatment decision
- Doubly-robust dynamic treatment regimen estimation via weighted least squares
- A general statistical framework for subgroup identification and comparative treatment scoring
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes
- Sparse Additive Models
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
- Quasi-oracle estimation of heterogeneous treatment effects
- Double/debiased machine learning for treatment and structural parameters
- Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes
- Bagging and deep learning in optimal individualized treatment rules
- Definitions, methods, and applications in interpretable machine learning
- Greedy outcome weighted tree learning of optimal personalized treatment rules
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Causal Inference Using Potential Outcomes
- Improved Doubly Robust Estimation in Learning Optimal Individualized Treatment Rules
- Variable Selection in Regression-Based Estimation of Dynamic Treatment Regimes
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