Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies

Cites Work

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Bagging predictors
BART: Bayesian additive regression trees
Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
Estimation of causal effects with multiple treatments: a review and new ideas
Using decision lists to construct interpretable and parsimonious treatment regimes
Tree-based methods for individualized treatment regimes
The central role of the propensity score in observational studies for causal effects
The role of the propensity score in estimating dose-response functions
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
Causal inference of interaction effects with inverse propensity weighting, G‐computation and tree‐based standardization
Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes
High-Dimensional Variable Selection for Survival Data
Covariate Balancing Propensity Score
Causal Inference With General Treatment Regimes
Random forests
Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups

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