- Ho, D.E., Imai, K., King, G., and Stuart, E.A. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software 42(8).
- Two-step process: does matching, then user does outcome analysis (integrated with Zelig package for R)
- Wide array of estimation procedures and matching methods available: nearest neighbor, Mahalanobis, caliper, exact, full, optimal, subclassification
- Built-in numeric and graphical diagnostics
- Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The Matching package for R. Journal of Statistical Software 42(7).
- Uses automated procedure to select matches, based on univariate and multivariate balance diagnostics
- Primarily 1:M matching (where M is a positive integer), allows matching with or without replacement, caliper, exact
- Includes built-in effect and variance estimation procedures
- Ridgeway, G., McCaffrey, D., and Morral, A. (2006). twang: Toolkit for weighting and analysis of nonequivalent groups.
- Functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights
- Primarily uses generalized boosted regression to estimate the propensity scores
- Includes functionality for multiple group weighting, marginal structural models
- Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking.
- Implements coarsened exact matching
- Can also be implemented through MatchIt
-Hansen, B.B., and Fredrickson, M. (2009). optmatch: Functions for optimal matching. - Variable ratio, optimal, and full matching - Can also be implemented through MatchIt
- Helmreich, J.E. and Pruzek, R.M. (2009). PSAgraphics: An R Package to Support Propensity Score Analysis. Journal of Statistical Software 29(6).
- From webpage: ?A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate.?
- Abadie, A., Diamond, A., and Hainmueller, H. (2011). Synth: An R Package for Synthetic Control Methods in Comparative Cast Studies. Journal of Statistical Software 42(13).
- Implements weighting approach to creating synthetic control groups
- Useful when there is a single treated unit, such as a state or country. Main idea is to form a weighted average of comparison units that, when weighted, looks- like the treated unit.
Cobalt: Covariate balance tables and plots
- Generates balance tables and figures for covariates following matching, weighting, or subclassification
- Integrated with MatchIt, twang, matching, CBPS, and ebal
- Imai, K., and Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society Series B 76(1): 243-263.
- Estimates propensity score in way that automatically targets balance
- Also includes functionality for marginal structural models, three- and four-valued treatment levels, and continuous treatments
ebal: Entropy reweighting to create balanced samples
- Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis 20: 25-46.
- Reweights dataset such that covariate distributions in reweighted data satisfy a set of user specified moment conditions.
PSweight: Supports propensity score weighting analysis of observational studies and randomized trials
- Li, Morgan and Zaslavsky (2018)
- Li and Li (2019)
- Enables the estimation and inference of average causal effects among target populations with binary and multiple treatments.