A Bayesian machine learning approach for estimating heterogeneous survivor causal effects: Applications to a critical care trial-Reference-Cited by-同舟云学术

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A Bayesian machine learning approach for estimating heterogeneous survivor causal effects: Applications to a critical care trial

Published:2024-03-01 Issue:1 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Chen Xinyuan¹,Harhay Michael O.²,Tong Guangyu³,Li Fan³

Affiliation:

1. Department of Mathematics and Statistics, Mississippi State University

2. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania

3. Department of Biostatistics, Yale School of Public Health

Publisher

Institute of Mathematical Statistics

Reference55 articles.

1. Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88 669–679.

2. HU, L., JI, J. and LI, F. (2021). Estimating heterogeneous survival treatment effect in observational data using machine learning. Stat. Med. 40 4691–4713.

3. WOODY, S., CARVALHO, C. M. and MURRAY, J. S. (2021). Model interpretation through lower-dimensional posterior summarization. J. Comput. Graph. Statist. 30 144–161.

4. TAN, Y. V. and ROY, J. (2019). Bayesian additive regression trees and the General BART model. Stat. Med. 38 5048–5069.

5. Ding, P. and Li, F. (2018). Causal inference: A missing data perspective. Statist. Sci. 33 214–237.

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