Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies-Reference-Cited by-同舟云学术

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

Published:2022-05-17 Issue:1 Volume:11 Page:
ISSN:2049-1573
Container-title:Stat
language:en
Short-container-title:Stat

Author:

Li Luo¹,Levine Richard A.²³^ORCID,Fan Juanjuan²³

Affiliation:

1. Computational Science Research Center San Diego State University San Diego California USA

2. Department of Mathematics and Statistics San Diego State University San Diego California USA

3. Analytics Studies and Institutional Research San Diego State University San Diego California USA

Funder

Office of Inspector General

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sta4.457

Reference54 articles.

1. A comparative study of subgroup identification methods for differential treatment effect: Performance metrics and recommendations

2. American Society of Clinical Oncology Provisional Clinical Opinion: Testing for KRAS Gene Mutations in Patients With Metastatic Colorectal Carcinoma to Predict Response to Anti–Epidermal Growth Factor Receptor Monoclonal Antibody Therapy

3. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

4. The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes

5. An Analysis of the Effectiveness of Supplemental Instruction: The Problem of Selection Bias and Limited Dependent Variables

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1. Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency;International Journal of Medical Informatics;2024-11

2. Heterogeneous treatment effect estimation for observational data using model-based forests;Statistical Methods in Medical Research;2024-02-08

3. How to Statistically Disentangle the Effects of Environmental Factors and Human Disturbances: A Review;Water;2023-02-13

4. Random Forest of Interaction Trees for Estimating Individualized Treatment Regimes with Ordered Treatment Levels in Observational Studies;Journal of Data Science;2023

5. Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model;Computational Intelligence and Neuroscience;2022-08-25

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