MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 16 articles )

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

  1. Du, Xin; Sun, Lei; Duivesteijn, Wouter; Nikolaev, Alexander; Pechenizkiy, Mykola: Adversarial balancing-based representation learning for causal effect inference with observational data (2021)
  2. Liangyuan Hu, Jiayi Ji: CIMTx: An R package for causal inference with multiple treatments using observational data (2021) arXiv
  3. Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky: dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference (2021) arXiv
  4. Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Jonathan Chen: Stratified Pilot Matching in R: The stratamatch Package (2020) arXiv
  5. Gunn, Laura H.; Gzyl, Henryk; ter Horst, Enrique; Ariza, Miller Janny; Molina, German: Maximum entropy in the mean methods in propensity score matching for interval and noisy data (2019)
  6. Zoe Meers, Robert Hickman, Thomas J. Leeper: ggparliament: A ggplot2 extension for parliament plotsin R (2019) not zbMATH
  7. Ding, Peng; Li, Fan: Causal inference: a missing data perspective (2018)
  8. Lenis, David; Ackerman, Benjamin; Stuart, Elizabeth A.: Measuring model misspecification: application to propensity score methods with complex survey data (2018)
  9. Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
  10. Sun, Lei; Nikolaev, Alexander G.: Mutual information based matching for causal inference with observational data (2016)
  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. Hill, Jennifer; Su, Yu-Sung: Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children’s cognitive outcomes (2013)
  13. Su, Xiaogang; Kang, Joseph; Fan, Juanjuan; Levine, Richard A.; Yan, Xin: Facilitating score and causal inference trees for large observational studies (2012)
  14. Baumert, Jürgen; Becker, Michael; Neumann, Marko; Nikolova, Roumiana: Early transition into the academic track of secondary schooling -- Transfer into a privileged learning environment? -- A comparison of regression analysis and propensity score matching (2009) MathEduc
  15. Imai, Kosuke; King, Gary; Nall, Clayton: Rejoinder: Matched pairs and the future of cluster-randomized experiments (2009)
  16. Stefano Iacus; Gary King; Giuseppe Porro: cem: Software for Coarsened Exact Matching (2009) not zbMATH