Matching

R package 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 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Dorie, Vincent; Hill, Jennifer; Shalit, Uri; Scott, Marc; Cervone, Dan: Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition (2019)
  2. Letham, Benjamin; Bakshy, Eytan: Bayesian optimization for policy search via online-offline experimentation (2019)
  3. Ding, Peng; Li, Fan: Causal inference: a missing data perspective (2018)
  4. Frölich, Markus; Huber, Martin; Wiesenfarth, Manuel: The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation (2017)
  5. Lopez, Michael J.; Gutman, Roee: Estimation of causal effects with multiple treatments: a review and new ideas (2017)
  6. Balzer, Laura; Ahern, Jennifer; Galea, Sandro; van der Laan, Mark: Estimating effects with rare outcomes and high dimensional covariates: knowledge is power (2016)
  7. Bolsinova, Maria; Maris, Gunter: A test for conditional independence between response time and accuracy (2016)
  8. 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
  9. Zwitser, Robert J.; Maris, Gunter: Ordering individuals with sum scores: the introduction of the nonparametric Rasch model (2016)
  10. Faraway, Julian J.: Linear models with R (2015)
  11. 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) not zbMATH
  12. Chakraborty, Bibhas; Moodie, Erica E. M.: Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine (2013)
  13. Sermaidis, Giorgos; Papaspiliopoulos, Omiros; Roberts, Gareth O.; Beskos, Alexandros; Fearnhead, Paul: Markov chain Monte Carlo for exact inference for diffusions (2013)
  14. Yu, Cindy; Legg, Jason; Liu, Bin: Estimating multiple treatment effects using two-phase semiparametric regression estimators (2013)
  15. Moodie, Erica E. M.; Chakraborty, Bibhas; Kramer, Michael S.: Q-learning for estimating optimal dynamic treatment rules from observational data (2012)
  16. Liu, Echu; Hsiao, Cheng; Matsumoto, Tomoya; Chou, Shinyi: Maternal full-time employment and overweight children: parametric, semi-parametric, and non-parametric assessment (2009)