Propensity Score Software (R)

R Code

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.

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.

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

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.

[Li, Morgan and Zaslavsky (2018)] (doi:10.1080/01621459.2016.1260466)
[Li and Li (2019)] (doi:10.1214/19-AOAS1282)
Enables the estimation and inference of average causal effects among target populations with binary and multiple treatments.

Last updated on May 27, 2020