Machine learning outcome regression improves doubly robust estimation of average causal effects-Reference-Cited by-同舟云学术

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Machine learning outcome regression improves doubly robust estimation of average causal effects

Published:2020-07-27 Issue:9 Volume:29 Page:1120-1133
ISSN:1053-8569
Container-title:Pharmacoepidemiology and Drug Safety
language:en
Short-container-title:Pharmacoepidemiol Drug Saf

Author:

Choi Byeong Yeob¹^ORCID,Wang Chen‐Pin¹,Gelfond Jonathan¹

Affiliation:

1. Department of Population Health Sciences UT Health San Antonio San Antonio Texas USA

Funder

National Cancer Institute

Publisher

Wiley

Subject

Pharmacology (medical),Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pds.5074

Reference24 articles.

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

2. Propensity Score Methods for Confounding Control in Nonexperimental Research

3. The central role of the propensity score in observational studies for causal effects

4. Estimation of Regression Coefficients When Some Regressors are not Always Observed

5. Rejoinder to adjusting for non‐ignorable drop‐out using semiparametric nonresponse models;Scharfstein DO;J Am Stat Assoc,1999

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1. Data Science Methods for Real-World Evidence Generation in Real-World Data;Annual Review of Biomedical Data Science;2024-08-23

2. Profiling compliers and noncompliers for instrumental variable analysis with covariates: A weighting approach;PLOS ONE;2023-06-15

3. Health status balancing weights for estimation of health care disparities;Health Services and Outcomes Research Methodology;2022-07-28

4. Instrumental variable estimation of truncated local average treatment effects;PLOS ONE;2021-04-05

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