Estimating individual treatment effects by gradient boosting trees
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Publication:6627236
DOI10.1002/sim.8357zbMATH Open1546.62719MaRDI QIDQ6627236
Shonosuke Sugasawa, Hisashi Noma
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
subgroup analysispotential outcomeprecision medicineindividual treatment effectsgradient boosting trees
Cites Work
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- Random survival forests
- Responder identification in clinical trials with censored data
- Comment on J. Neyman and causal inference in experiments and observational studies: On the application of probability theory to agricultural experiments. Essay on principles. Section 9 [Ann. Agric. Sci. 10 (1923), 1--51]
- Tree based weighted learning for estimating individualized treatment rules with censored data
- Combining biomarkers to optimize patient treatment recommendations
- Recursive partitioning for heterogeneous causal effects
- Analysis of randomized comparative clinical trial data for personalized treatment selections
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
- The central role of the propensity score in observational studies for causal effects
- A general statistical framework for subgroup identification and comparative treatment scoring
- 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
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Random forests
- Stochastic gradient boosting.
- The Elements of Statistical Learning
- Estimating optimal treatment regimes from a classification perspective
Related Items (3)
Rule ensemble method with adaptive group Lasso for heterogeneous treatment effect estimation ⋮ A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data ⋮ Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
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