R
MatchIt http://gking.harvard.edu/matchit
Ho, D.E., Imai, K., King, G., and Stuart, E.A. (2011). MatchIt: Nonparametric preprocessing for parameteric causal inference. Journal of Statistical Software 42(8). http://www.jstatsoft.org/v42/i08
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
Matching http://sekhon.berkeley.edu/matching
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). http://www.jstatsoft.org/v42/i07
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
twang http://cran.r-project.org/web/packages/twang/index.html
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
cem http://gking.harvard.edu/cem/
Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.
Implements coarsened exact matching
Can also be implemented through MatchIt
optmatch http://cran.r-project.org/web/packages/optmatch/index.html
Hansen, B.B., and Fredrickson, M. (2009). optmatch: Functions for optimal matching.
Variable ratio, optimal, and full matching
Can also be implemented through MatchIt
PSAgraphics http://cran.r-project.org/web/packages/PSAgraphics/index.html
Helmreich, J.E. and Pruzek, R.M. (2009). PSAgraphics: An R Package to Support Propensity Score Analysis. Journal of Statistical Software 29(6). Available here.
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.”
Synth https://cran.r-project.org/web/packages/Synth/
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). http://www.jstatsoft.org/v42/i13
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 https://cran.r-project.org/web/packages/cobalt/index.html
Generates balance tables and figures for covariates following matching, weighting, or subclassification
Integrated with MatchIt, twang, matching, CBPS, and ebal
CBPS https://cran.r-project.org/web/packages/CBPS/index.html
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 https://cran.r-project.org/web/packages/ebal/index.html
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.
Stata
teffects suite http://www.stata.com/manuals13/te.pdf
Stata written causal inference commands for matching and weighting
Includes balance diagnostics, 1:1 matching, weighting, doubly robust approaches
Leuven, E. and Sianesi, B. (2003). psmatch2. Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing.
Allows k:1 matching, kernel weighting, Mahalanobis matching
Includes built-in diagnostics
Includes procedures for estimating ATT or ATE
pscore http://www.lrz-muenchen.de/~sobecker/pscore.html
Becker, S.O. and Ichino, A. (2002). Estimation of average treatment effects based on propensity scores (2002) The Stata Journal 2(4): 358-377.
k:1 matching, radius (caliper) matching, and stratification (subclassification)
For estimating the ATT
match http://www.economics.harvard.edu/faculty/imbens/software_imbens
Abadie, A., Drukker, D., Herr, J. L., and Imbens, G. W. (2004). Implementing matching estimators for average treatment effects in Stata. The Stata Journal 4(3): 290-311. Available here.
Primarily k:1 matching (with replacement)
Allows estimation of ATT or ATE, including robust variance estimators
cem http://gking.harvard.edu/cem/
Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.
Implements coarsened exact matching
SAS
SAS usage note: http://support.sas.com/kb/30/971.html
Local and global optimal propensity score matching
Coca-Perraillon, M. (2007). Local and global optimal propensity score matching. In SAS Global Forum 2007. Paper 185-2007. Available here.
Variety of matching methods. No built in diagnostics. Assumes propensity score already estimated.
cem http://gking.harvard.edu/cem/
Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.
Implements coarsened exact matching
Greedy matching (1:1 nearest neighbor)
Parsons, L. S. (2001). Reducing bias in a propensity score matched-pair sample using greedy matching techniques. In SAS SUGI 26, Paper 214-26. Available here.
Parsons, L.S. (2005). Using SAS software to perform a case-control match on propensity score in an observational study. In SAS SUGI 30, Paper 225-25. Available here.
Kosanke, J., and Bergstralh, E. (2004). gmatch: Match 1 or more controls to cases using the GREEDY algorithm. http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros
1:1 Mahalanbois matching within propensity score calipers
Feng, W.W., Jun, Y., and Xu, R. (2005). A method/macro based on propensity score and Mahalanobis distance to reduce bias in treatment comparison in observational study. www.lexjansen.com/pharmasug/2006/publichealthresearch/pr05.pdf
Weighting
Leslie, S. and Thiebaud, P. (2006). Using propensity scores to adjust for treatment selection bias. http://www.lexjansen.com/wuss/2006/Analytics/ANL-Leslie.pdf
Variable ratio matching, optimal matching algorithm
Kosanke, J., and Bergstralh, E. (2004). Match cases to controls using variable optimal matching. http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros
SPSS
Propensity score matching in SPSS http://arxiv.org/ftp/arxiv/papers/1201/1201.6385.pdf
Thoemmes, F. (2012). Propensity score matching in SPSS. http://sourceforge.net/projects/psmspss/files/
Nearest neighbor propensity score matching with various options (with/without replacement, calipers, k to 1, etc.)
Detailed balance statistics and graphs
Actually calls MatchIt using a point and click interface
Software for performing analyses of sensitivity to an unobserved confounder
R
rbounds: An R package for sensitivity analysis with matched data (L. Keele). http://www.personal.psu.edu/ljk20/rbounds.html
sensitivity function in twang package (G. Ridgeway et al.). http://rss.acs.unt.edu/Rdoc/library/twang/html/sensitivity.html
Stata
sensatt: A simulation-based sensitivity analysis for matching estimators (T. Nannicini) http://www.tommasonannicini.eu/Portals/0/sensatt_wp_4.pdf
Nannicini T. (2007). A Simulation-Based Sensitivity Analysis for Matching Estimators. Stata Journal, 7(3), 334-350
Ichino A., Mealli F., Nannicini T. (2008). From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity? Journal of Applied Econometrics, 23(3), 305-327.
Excel
Love, T.E. (2008) Spreadsheet-based sensitivity analysis calculations for matched samples. Center for Health Care Research & Policy, Case Western Reserve University. http://www.chrp.org/propensity/ , http://www.chrp.org/propensity/sensitivitydocumentation.pdf , http://www.chrp.org/propensity/sensitivityspreadsheet.xls
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