Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data
From MaRDI portal
Publication:6180733
DOI10.1080/10618600.2022.2141752arXiv2108.08415MaRDI QIDQ6180733
No author found.
Publication date: 22 January 2024
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.08415
cross-validationtransportabilityaugmented inverse probability weightingclassification errorcovariate shiftcalibration weighting
Cites Work
- Unnamed Item
- Unnamed Item
- Estimating treatment effect heterogeneity in randomized program evaluation
- Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine
- Performance guarantees for individualized treatment rules
- Solving a class of linearly constrained indefinite quadratic problems by DC algorithms
- Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
- Robustifying trial-derived optimal treatment rules for a target population
- Using decision lists to construct interpretable and parsimonious treatment regimes
- Combining biomarkers to optimize patient treatment recommendations
- Tree-based methods for individualized treatment regimes
- Robust Truncated Hinge Loss Support Vector Machines
- Updating Quasi-Newton Matrices with Limited Storage
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Testing for Qualitative Interactions between Treatment Effects and Patient Subsets
- A Robust Method for Estimating Optimal Treatment Regimes
- Learning Optimal Distributionally Robust Individualized Treatment Rules
- Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals
- Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations
- Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores
- Convexity, Classification, and Risk Bounds
- A Generalization of Sampling Without Replacement From a Finite Universe
- Improving Trial Generalizability Using Observational Studies
This page was built for publication: Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data