Matching: Multivariate and Propensity Score Matching with Balance Optimization. Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided.

References in zbMATH (referenced in 10 articles )

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  1. Lopez, Michael J.; Gutman, Roee: Estimation of causal effects with multiple treatments: a review and new ideas (2017)
  2. Lindlbauer, Ivonne; Schreyögg, Jonas; Winter, Vera: Changes in technical efficiency after quality management certification: A DEA approach using difference-in-difference estimation with genetic matching in the hospital industry (2016) ioport
  3. Zwitser, Robert J.; Maris, Gunter: Ordering individuals with sum scores: the introduction of the nonparametric Rasch model (2016)
  4. Faraway, Julian J.: Linear models with R (2015)
  5. Jenny Häggström and Emma Persson and Ingeborg Waernbaum and Xavier de Luna: CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects (2015)
  6. Chakraborty, Bibhas; Moodie, Erica E. M.: Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine (2013)
  7. Sermaidis, Giorgos; Papaspiliopoulos, Omiros; Roberts, Gareth O.; Beskos, Alexandros; Fearnhead, Paul: Markov chain Monte Carlo for exact inference for diffusions (2013)
  8. Yu, Cindy; Legg, Jason; Liu, Bin: Estimating multiple treatment effects using two-phase semiparametric regression estimators (2013)
  9. Moodie, Erica E. M.; Chakraborty, Bibhas; Kramer, Michael S.: Q-learning for estimating optimal dynamic treatment rules from observational data (2012)
  10. Liu, Echu; Hsiao, Cheng; Matsumoto, Tomoya; Chou, Shinyi: Maternal full-time employment and overweight children: parametric, semi-parametric, and non-parametric assessment (2009)