Combining Multiple Observational Data Sources to Estimate Causal Effects
Author:
Affiliation:
1. Department of Statistics, North Carolina State University, Raleigh, NC;
2. Department of Statistics, University of California, Berkeley, CA
Funder
National Science Foundation
National Cancer Institute
Directorate for Mathematical and Physical Sciences
Division of Mathematical Sciences
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2019.1609973
Reference76 articles.
1. Large Sample Properties of Matching Estimators for Average Treatment Effects
2. On the Failure of the Bootstrap for Matching Estimators
3. Matching on the Estimated Propensity Score
4. Identification of Causal Effects Using Instrumental Variables
5. Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research
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