Package: MatchIt 4.7.2

Noah Greifer

MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <doi:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)

Authors:Daniel Ho [aut], Kosuke Imai [aut], Gary King [aut], Elizabeth Stuart [aut], Alex Whitworth [ctb], Noah Greifer [cre, aut]

MatchIt_4.7.2.tar.gz
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MatchIt_4.7.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MatchIt/json (API)

# Install 'MatchIt' in R:
install.packages('MatchIt', repos = c('https://kosukeimai.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kosukeimai/matchit/issues

Pkgdown/docs site:https://kosukeimai.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • lalonde - Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).

On CRAN:

Conda:

cppopenmp

14.66 score 236 stars 23 packages 3.5k scripts 23k downloads 358 mentions 9 exports 7 dependencies

Last updated from:bccb2fcba7. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK540
linux-devel-x86_64OK456
source / vignettesOK391
linux-release-arm64OK260
linux-release-x86_64OK291
macos-release-arm64OK132
macos-release-x86_64OK378
macos-oldrel-arm64OK141
macos-oldrel-x86_64OK286
windows-develOK206
windows-releaseOK242
windows-oldrelOK204
wasm-releaseOK259

Exports:add_s.weightseuclidean_distget_matchesmahalanobis_distmatch_datamatch.datamatchitrobust_mahalanobis_distscaled_euclidean_dist

Dependencies:backportschkclilifecycleRcppRcppProgressrlang

Matching Methods
Introduction | Matching | Nearest Neighbor Matching (method = "nearest") | Optimal Pair Matching (method = "optimal") | Optimal Full Matching (method = "full") | Generalized Full Matching (method = "quick") | Genetic Matching (method = "genetic") | Exact Matching (method = "exact") | Coarsened Exact Matching (method = "cem") | Subclassification (method = "subclass") | Cardinality and Profile Matching (method = "cardinality") | Customizing the Matching Specification | Specifying the propensity score or other distance measure (distance) | Implementing common support restrictions (discard) | Caliper matching (caliper) | Mahalanobis distance matching (mahvars) | Exact matching (exact) | Anti-exact matching (antiexact) | Matching with replacement (replace) | $k$:1 matching (ratio) | Matching order (m.order) | Choosing a Matching Method | Reporting the Matching Specification | References
Rendered frommatching-methods.Rmdusingknitr::rmarkdown_notangle

Last update: 2025-05-29
Started: 2020-11-04

Estimating Effects After Matching
Introduction | Identifying the estimand | G-computation | Modeling the Outcome | Estimating Standard Errors and Confidence Intervals | Robust and Cluster-Robust Standard Errors | Bootstrapping | Estimating Treatment Effects and Standard Errors After Matching | The Standard Case | Adjustments to the Standard Case | Matching for the ATE | Matching with replacement | Matching without pairing | Propensity score subclassification | Binary outcomes | Survival outcomes | Using Bootstrapping to Estimate Confidence Intervals | The standard bootstrap | The cluster bootstrap | Moderation Analysis | Reporting Results | Common Mistakes | 1. Failing to include weights | 2. Failing to use robust or cluster-robust standard errors | 3. Interpreting conditional effects as marginal effects | References | Code to Generate Data used in Examples
Rendered fromestimating-effects.Rmdusingknitr::rmarkdown_notangle

Last update: 2025-03-14
Started: 2020-11-04

Matching with Sampling Weights
Introduction | Matching | Assessing Balance | Estimating the Effect | Code to Generate Data used in Examples | References
Rendered fromsampling-weights.Rmdusingknitr::rmarkdown_notangle

Last update: 2025-03-14
Started: 2020-11-04

MatchIt: Getting Started
Introduction | Planning | Check Initial Imbalance | Matching | Assessing the Quality of Matches | Trying a Different Matching Specification | Estimating the Treatment Effect | Reporting Results | Conclusion | References
Rendered fromMatchIt.Rmdusingknitr::rmarkdown_notangle

Last update: 2025-03-14
Started: 2020-11-04

Assessing Balance
Introduction | Recommendations for Balance Assessment | Recommendations for Balance Reporting | Assessing Balance with MatchIt | summary.matchit() | plot.summary.matchit() | plot.matchit() | Assessing Balance After Subclassification | Assessing Balance with cobalt | bal.tab() | love.plot() | bal.plot() | Conclusion | References
Rendered fromassessing-balance.Rmdusingknitr::rmarkdown_notangle

Last update: 2025-01-10
Started: 2020-11-04