Network flow methods for the minimum covariate imbalance problem
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Publication:2116900
DOI10.1016/j.ejor.2021.10.041zbMath1506.62249OpenAlexW3208420742WikidataQ114184403 ScholiaQ114184403MaRDI QIDQ2116900
Jason J. Sauppe, Xu Rao, Dorit S. Hochbaum
Publication date: 18 March 2022
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2021.10.041
Integer programming (90C10) Deterministic network models in operations research (90B10) Causal inference from observational studies (62D20)
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Cites Work
- Matching methods for causal inference: a review and a look forward
- Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes
- Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies
- Beyond the flow decomposition barrier
- Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
- The role of covariate balance in observational studies
- Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
- Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies
- Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
- Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer
- On some techniques useful for solution of transportation network problems
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