matchit: MatchIt: Matching Software for Causal Inference

Description

matchit is the main command of the package MatchIt, which enables parametric models for causal inference to work better by selecting well-matched subsets of the original treated and control groups. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. Matched data sets created by MatchIt can be entered easily in Zelig (http://gking.harvard.edu/zelig) for subsequent parametric analyses. Full documentation is available online at http://gking.harvard.edu/matchit, and help for specific commands is available through help.matchit.

Usage

matchit(formula, data, method = "nearest", distance = "logit",
               distance.options = list(), discard = "none",
               reestimate = FALSE, ...)

Arguments

formula

This argument takes the usual syntax of R formula, treat ~ x1 + x2, where treat is a binary treatment indicator and x1 and x2 are the pre-treatment covariates. Both the treatment indicator an

data

This argument specifies the data frame containing the variables called in formula.

method

This argument specifies a matching method. Currently, "exact" (exact matching), "full" (full matching), "genetic" (genetic matching), "nearest" (nearest neighbor matching), "optimal"

distance

This argument specifies the method used to estimate the distance measure. The default is logistic regression, "logit". A variety of other methods are available. See http://gking.harvard.edu/matchit/docs/All_Matching_Methods.h

distance.options

This optional argument specifies the optional arguments that are passed to the model for estimating the distance measure. The input to this argument should be a list.

discard

This argument specifies whether to discard units that fall outside some measure of support of the distance score before matching, and not allow them to be used at all in the matching procedure. Note that discarding units may change the quanti

reestimate

This argument specifies whether the model for distance measure should be re-estimated after units are discarded. The input must be a logical value. The default is FALSE.

...

Additional arguments to be passed to a variety of matching methods. See http://gking.harvard.edu/matchit/?? for the complete list of options.

Value

callThe original matchit call.
formulaThe formula used to specify the model for estimating the distance measure.
modelThe output of the model used to estimate the distance measure. summary(m.out$model) will give the summary of the model where m.out is the output object from matchit.
match.matrixAn $n_1$ by ratio matrix where the row names, which can be obtained through row.names(match.matrix), represent the names of the treatment units, which come from the data frame specified in data. Each column stores the name(s) of the control unit(s) matched to the treatment unit of that row. For example, when the ratio input for nearest neighbor or optimal matching is specified as 3, the three columns of match.matrix represent the three control units matched to one treatment unit). NA indicates that the treatment unit was not matched.
discardedA vector of length $n$ that displays whether the units were ineligible for matching due to common support restrictions. It equals TRUE if unit $i$ was discarded, and it is set to FALSE otherwise.
distanceA vector of length $n$ with the estimated distance measure for each unit.
weightsA vector of length $n$ that provides the weights assigned to each unit in the matching process. Unmatched units have weights equal to 0. Matched treated units have weight 1. Each matched control unit has weight proportional to the number of treatment units to which it was matched, and the sum of the control weights is equal to the number of uniquely matched control units. See http://gking.harvard.edu/matchit/docs/How_Exactly_are.html for more details.
subclassThe subclass index in an ordinal scale from 1 to the total number of subclasses as specified in subclass (or the total number of subclasses from full or exact matching). Unmatched units have NA.
q.cutThe subclass cut-points that classify the distance measure.
treatThe treatment indicator from data (the left-hand side of formula).
XThe covariates used for estimating the distance measure (the right-hand side of formula).

Details

The matching is done using the matchit(treat ~ X, ...) command, where treat is the vector of treatment assignments and X are the covariates to be used in the matching. There are a number of matching options, detailed below. The full syntax is

matchit(formula, data=NULL, discard=0, exact=FALSE, replace=FALSE,
ratio=1, model="logit", reestimate=FALSE, nearest=TRUE, m.order=2,
caliper=0, calclosest=FALSE, mahvars=NULL, subclass=0, sub.by="treat",
counter=TRUE, full=FALSE, full.options=list(), ...)

A summary of the results can be seen graphically using plot(matchitobject), or numerically using summary(matchitobject). print(matchitobject) also prints out the output.

References