One important detail that may not be clear from the answer above is that the default form of matching in the matchit package (and in much of the scholarly literature in any field) is to use a propensity score that estimates, for each observation, the probability of assignment to treatment given some set of pre-treatment covariates using logistic regression.

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method_full for optimal full matching, which is similar to subclassification except that the number of subclasses and subclass membership are chosen to optimize the within-subclass distance. Examples matchit.exec(url, match) Returns: Object. Returns an object an object of key:val pairs, as defined by your route pattern. url. Type: String. The URL (pathname) to evaluate. Important: This should be pathnames only as any querystrings will be included the response.

Coarsened exact matching (i.e., matchit() with method = "cem") has been completely rewritten and no longer involves the cem package, eliminating some spurious warning messages and fixing some bugs. All the same arguments can still be used, so old code will run, though some results will differ slightly.

MatchIt: Nonparametric Preprocessing for Parametric Causal Inference Overview. MatchIt provides a simple and straightforward interface to various methods of matching for covariate balance in observational studies. Matching is one way to reduce confounding and model dependence when estimating treatment effects. An Update on The MatchIt Package in R Basic Use of MatchIt.

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Matching is one way to reduce confounding and model dependence when estimating treatment effects.

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt provides a simple and straightforward interface to various methods of matching for covariate balance in observational studies. Matching is one way to reduce confounding and model dependence when estimating treatment effects. Several matching methods are available, including nearest neighbor matching, optimal pair matching, optimal full matching, genetic matching, exact matching, coarsened exact matching, and subclassification, some of which rely on functions from other R packages.
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dloewenstein / packages / r-matchit 3.0.2 0 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.

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MatchIt provides a simple and straightforward interface to various methods of matching for covariate balance in observational studies. Matching is one way to reduce confounding and model dependence when estimating treatment effects.

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