Understanding Causal Distributional and Subgroup Effects With the Instrumental Propensity Score-Reference-Cited by-同舟云学术

Understanding Causal Distributional and Subgroup Effects With the Instrumental Propensity Score

Published:2017-08-17 Issue:3 Volume:187 Page:614-622
ISSN:0002-9262
Container-title:American Journal of Epidemiology
language:en
Short-container-title:

Author:

Cheng Jing¹,Lin Winston²

Affiliation:

1. Division of Oral Epidemiology and Dental Public Health, Department of Preventive and Restorative Dental Sciences, University of California at San Francisco, San Francisco, California

2. Department of Statistics and Data Science, Yale University, New Haven, Connecticut

Funder

National Institute of Mental Health

National Institute on Drug Abuse

Publisher

Oxford University Press (OUP)

Subject

Epidemiology

Link

http://academic.oup.com/aje/article-pdf/187/3/614/25450641/kwx282.pdf

Reference50 articles.

1. The central role of the propensity score in observational studies for causal effects;Rosenbaum;Biometrika,1983

2. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group;D’Agostino;Stat Med,1998

3. Matching methods for causal inference: a review and a look forward;Stuart;Stat Sci,2010

4. Identification of causal effects using instrumental variables;Angrist;J Am Stat Assoc,1996

5. Bootstrap tests for distributional treatment effects in instrumental variable models;Abadie;J Am Stat Assoc,2002

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Instrumental variable analysis for cost outcome: Application to the effect of primary care visit on medical cost among low‐income adults;Statistics in Medicine;2023-08-07

2. Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single‐point exposure;Statistics in Medicine;2022-12-23

3. Comparative effectiveness research considered methodological insights from simulation studies in physician's prescribing preference;Journal of Clinical Epidemiology;2022-08

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

5. The impact of dialysis on critically ill elderly patients with acute kidney injury: an analysis by propensity score matching;Brazilian Journal of Nephrology;2019-03