Description Usage Arguments Details Value Author(s) References See Also
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
.
1 2 3 |
formula |
This argument takes the usual syntax of R formula,
|
data |
This argument specifies the data frame containing the
variables called in |
method |
This argument specifies a matching method. Currently,
|
distance |
This argument specifies the method used to estimate the
distance measure. The default is logistic regression,
|
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 quantity of
interest being estimated. The options are: |
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
|
... |
Additional arguments to be passed to a variety of matching methods. |
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.
call |
The original |
formula |
The formula used to specify the model for estimating the distance measure. |
model |
The output of the model used to estimate
the distance measure. |
match.matrix |
An n_1 by |
discarded |
A vector of length $n$ that displays
whether the units were ineligible for matching due to common
support restrictions. It equals |
distance |
A vector of length n with the estimated distance measure for each unit. |
weights |
A vector of length n that provides the
weights assigned to each unit in the matching process. Unmatched
units have weights equal to |
subclass |
The subclass index in an ordinal
scale from 1 to the total number of subclasses as specified in
|
q.cut |
The subclass cut-points that classify the distance measure. |
treat |
The treatment indicator from
|
X |
The covariates used for estimating the
distance measure (the right-hand side of |
nn |
A basic summary table of matched data (e.g., the number of matched units) |
Daniel Ho daniel.ho@yale.edu; Kosuke Imai kimai@princeton.edu; Gary King king@harvard.edu; Elizabeth Stuartestuart@jhsph.edu
Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15(3): 199-236. http://gking.harvard.edu/files/abs/matchp-abs.shtml
Please use help.matchit
to access the matchit reference
manual. The complete document is available online at
http://gking.harvard.edu/matchit.
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