IDEAS home Printed from https://ideas.repec.org/r/taf/jnlasa/v107y2012i500p1360-1371.html
   My bibliography  Save this item

Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
  2. Eric Dunipace, 2021. "Optimal transport weights for causal inference," Papers 2109.01991, arXiv.org, revised May 2022.
  3. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
  4. Md Saiful Islam & Md Sarowar Morshed & Md. Noor-E-Alam, 2022. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3023-3041, November.
  5. Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
  6. José R. Zubizarreta, 2015. "Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 910-922, September.
  7. Bikram Karmakar, 2022. "An approximation algorithm for blocking of an experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1726-1750, November.
  8. Bo Zhang, 2023. "Efficient algorithms for building representative matched pairs with enhanced generalizability," Biometrics, The International Biometric Society, vol. 79(4), pages 3981-3997, December.
  9. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
  10. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
  11. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
  12. Tian Heong Chan & Francis de Véricourt & Omar Besbes, 2019. "Contracting in Medical Equipment Maintenance Services: An Empirical Investigation," Management Science, INFORMS, vol. 65(3), pages 1136-1150, March.
  13. Díaz, Juan & Grau, Nicolás & Reyes, Tatiana & Rivera, Jorge, 2021. "The impact of grade retention on juvenile crime," Economics of Education Review, Elsevier, vol. 84(C).
  14. Md Saiful Islam & Md Sarowar Morshed & Gary J Young & Md Noor-E-Alam, 2019. "Robust policy evaluation from large-scale observational studies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
  15. Hochbaum, Dorit S. & Rao, Xu & Sauppe, Jason, 2022. "Network flow methods for the minimum covariate imbalance problem," European Journal of Operational Research, Elsevier, vol. 300(3), pages 827-836.
  16. Bryan Keller & Elizabeth Tipton, 2016. "Propensity Score Analysis in R," Journal of Educational and Behavioral Statistics, , vol. 41(3), pages 326-348, June.
  17. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
  18. Colin B. Fogarty & Mark E. Mikkelsen & David F. Gaieski & Dylan S. Small, 2016. "Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 447-458, April.
  19. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
  20. Paul R. Rosenbaum, 2023. "Sensitivity analyses informed by tests for bias in observational studies," Biometrics, The International Biometric Society, vol. 79(1), pages 475-487, March.
  21. Harsh Parikh & Cynthia Rudin & Alexander Volfovsky, 2018. "MALTS: Matching After Learning to Stretch," Papers 1811.07415, arXiv.org, revised Jun 2023.
  22. María de los Angeles Resa & José R. Zubizarreta, 2020. "Direct and stable weight adjustment in non‐experimental studies with multivalued treatments: analysis of the effect of an earthquake on post‐traumatic stress," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1387-1410, October.
  23. Ruoqi Yu, 2023. "How well can fine balance work for covariate balancing," Biometrics, The International Biometric Society, vol. 79(3), pages 2346-2356, September.
  24. Marco Morucci & Md. Noor-E-Alam & Cynthia Rudin, 2022. "A Robust Approach to Quantifying Uncertainty in Matching Problems of Causal Inference," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 156-171, October.
  25. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
  26. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.
  27. Glazer Amanda K. & Pimentel Samuel D., 2023. "Robust inference for matching under rolling enrollment," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-19, January.
  28. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.