Causal inference with noisy data: Bias analysis and estimation approaches to simultaneously addressing missingness and misclassification in binary outcomes-Reference-Cited by-同舟云学术

Causal inference with noisy data: Bias analysis and estimation approaches to simultaneously addressing missingness and misclassification in binary outcomes

Published:2019-12-05 Issue:4 Volume:39 Page:456-468
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Shu Di¹²^ORCID,Yi Grace Y.³²^ORCID

Affiliation:

1. Department of Population MedicineHarvard Medical School and Harvard Pilgrim Health Care Institute Boston Massachusetts

2. Department of Statistics and Actuarial ScienceUniversity of Waterloo Waterloo Ontario Canada

3. Department of Statistical and Actuarial SciencesDepartment of Computer Science, University of Western Ontario London Ontario Canada

Funder

The Natural Sciences and Engineering Research Council of Canada

A Collaborative Research Team Project of Canadian Statistical Sciences Institute

The Canada Research Chairs Program

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8419

Reference46 articles.

1. Causal Inference for Statistics, Social, and Biomedical Sciences

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

3. Model-Based Direct Adjustment

4. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

Cited by 2 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. A Capture–Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes;Epidemiology;2024-03-04

2. Testing of Poisson mean with under-reported counts;Brazilian Journal of Probability and Statistics;2021-07-22