Academic Journal
Robust policy evaluation from large-scale observational studies.
| Τίτλος: | Robust policy evaluation from large-scale observational studies. |
|---|---|
| Συγγραφείς: | Islam MS; Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, United States of America., Morshed MS; Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, United States of America., Young GJ; Center for Health Policy and Healthcare Research, Northeastern University, Boston, Massachusetts, United States of America.; D'Amore-McKim School of Business, Northeastern University, Boston, Massachusetts, United States of America.; Bouvè College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America., Noor-E-Alam M; Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, United States of America.; Center for Health Policy and Healthcare Research, Northeastern University, Boston, Massachusetts, United States of America. |
| Πηγή: | PloS one [PLoS One] 2026 Jun 30; Vol. 21 (6), pp. e0348228. Date of Electronic Publication: 2026 Jun 30 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Observational Studies as Topic* , Policy Making*, Algorithms ; Humans |
| Περίληψη: | Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP does not have a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies. (Copyright: © 2026 Islam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| Entry Date(s): | Date Created: 20260630 Date Completed: 20260630 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13318056 |
| DOI: | 10.1371/journal.pone.0348228 |
| PMID: | 42378205 |
| Βάση Δεδομένων: | MEDLINE |
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