Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies-Reference-Cited by-同舟云学术

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Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies

Published:2020-05-22 Issue:4 Volume:29 Page:744-757
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Bennett Magdalena¹,Vielma Juan Pablo²,Zubizarreta José R.³

Affiliation:

1. Department of Education Policy and Social Analysis, Teachers College at Columbia University, New York, NY;

2. Operations Research and Statistics Group, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA;

3. Department of Health Care Policy and Department of Statistics, Harvard University, Boston, MA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2020.1753532

Reference90 articles.

1. On the Failure of the Bootstrap for Matching Estimators

2. Mixed Integer Programming: Analyzing 12 Years of Progress

3. Does Regression Produce Representative Estimates of Causal Effects?

4. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants

5. The perfectly matchable subgraph polytope of a bipartite graph

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1. Patient recruitment using electronic health records under selection bias: A two-phase sampling framework;The Annals of Applied Statistics;2024-09-01

2. Defining, identifying, and estimating causal effects with the potential outcomes framework: a review for education research;Asia Pacific Education Review;2024-05-10

3. Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot;The American Statistician;2024-02-08

4. Targeted learning in observational studies with multi‐valued treatments: An evaluation of antipsychotic drug treatment safety;Statistics in Medicine;2024-02-05

5. A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model;Proceedings of the 52nd International Conference on Parallel Processing Workshops;2023-08-07

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