MatchBalance {Matching} | R Documentation |
This function provides a variety of univariate and multivariate tests
to determine if balance exists. The function does no matching.
Matching is performed by the Match
function.
MatchBalance
is used to determine if Match
was
successful in achieving balance. Multivariate balance is determined
by the use of a model. This approach can be used regardless of the
algorithm used to do the original matching. For example, even if
Match
was told NOT to use a propensity score,
MatchBalance
can be used to test balance.
MatchBalance(formul, data = NULL, match.out = NULL, ks = TRUE, mv = FALSE, nboots=1000, nmc=nboots, maxit = 1000, weights=rep(1,nrow(data)), digits=5, verbose=1, paired=TRUE, ...)
formul |
A formula denoting the model for which balance should be determined. This model will be estimated by a binary logistic estimator. The dependent variable in the formula is usually the treatment indicator. Univariate balance tests will be conducted for each of the regressors included in this model. And the multivariate tests will be conducted on the predicted probabilities of treatment for both treated and control based on this formula. The predicted probability densities for both treated and control should be indistinguishable if balance has been achieved. Note that the model defined by this formula is estimated separately for the matched and unmatched datasets. |
data |
A data frame which contains all of the variables in the formula. If a data frame is not provided, the variables are obtained via lexical scoping. |
match.out |
The output object from the Match
function. If this output is included, MatchBalance will provide
balance tests for both before and after matching. Otherwise
balance tests will only be conducted for the raw data. |
ks |
A logical flag for if the univariate bootstrap
Kolmogorov-Smirnov (KS) test should be calculated. If the ks option
is set to true, the univariate KS test is calculated for all
non-dichotomous variables. The bootstrap KS test is consistent even
for non-continuous variables. See ks.boot for more
details. |
mv |
A logical flag for if multivariate balance tests (the Kolmogorov-Smirnov and Chi-Square tests) should be calculated. |
maxit |
The maximum number of iterations for the glm logistic procedure. |
weights |
A vector of observation specific weights. |
nboots |
The number of bootstrap samples to be run. If zero, no
bootstraps are done. Bootstrapping is highly recommended because
the Kolmogorov-Smirnov test only provides correct coverage when
bootstrapped due to the existence of nuisance parameters. At least
500 nboots (preferably 1000) are recommended for publication
quality p-values. Also see the nmc option. |
nmc |
The number of Monte Carlo simulations to be conducted for
each Kolmogorov-Smirnov test calculated. Monte Carlo simulations
are highly recommended because the usual Kolmogorov-Smirnov test is not
consistent when the densities being compared contain point masses. At least
500 nmc (preferably 1000) are recommended for publication
quality p-values. Also see the nboots option. |
digits |
The number of significant digits that should be displayed. |
verbose |
The amount of printing to be done. If zero, there is no printing. If one, the results are summarized. If two, details are printing such as the number of the bootstrap sample currently being estimated. |
paired |
A flag for if the paired t.test should be
used after matching. Regardless of the value of this option, an
unpaired t.test is done for the unmatched data because
it is assumed that the unmatched data were not generated by a paired
experiment. |
... |
Further arguments passed to balanceMV . |
The function can be used to determine if matching was successful in
achieving balance. Two univariate tests are provided: the t-test and
the bootstrap Kolmogorov-Smirnov (KS) test. The bootstrap KS test is
highly recommended (see the ks
and nboots
options)
because the bootstrap KS is consistent even for non-continuous
covariates, and it is more powerful than the t-test. Before matching
the two sample t-test is used, and after matching the paired t-test
is used.
Two multivariate tests are provided. The KS and Chi-Square null deviance tests. The KS test is to be preferred over the Chi-Square test because the Chi-Square test is not testing the relevant hypothesis. The null hypothesis for the KS test is of equal balance in the estimated probabilities between treated and control. The null hypothesis for the Chi-Square test, however, is of all of the parameters being insignificant; a comparison of residual versus null deviance. If the covariates being considered are discrete, this KS test is asymptotically nonparametric as long as the logit model does not produce zero parameter estimates. The bootstrap-Monte Carlo version of the KS test is highly recommended because the usual KS test is not consistent when there are point masses in the distributions being compared, and the bootstrap is needed because parameters are being estimated in the logit model.
mv |
A return object from a call to balanceMV |
uv |
A return object from a call to balanceUV . The
univariate tests performed on the last variable in formul are
returned. For the other variables call balanceUV
directly. Note that the univariate test results for all of the
variables in formul are printed if verbose > 1 . |
Jasjeet S. Sekhon, Harvard University, jasjeet_sekhon@harvard.edu, http://jsekhon.fas.harvard.edu/
Sekhon, Jasjeet S. 2004. ``Balance Tests for Matching Estimators.'' Working Paper. http://jsekhon.fas.harvard.edu/matching
Abadie, Alberto. 2002. ``Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models.'' Journal of the American Statistical Association, 97:457 (March) 284-292.
Hall, Peter. 1992. The Bootstrap and Edgeworth Expansion. New York: Springer-Verlag.
Wilcox, Rand R. 1997. Introduction to Robust Estimation. San Diego, CA: Academic Press.
William J. Conover (1971), Practical nonparametric statistics. New York: John Wiley & Sons. Pages 295-301 (one-sample "Kolmogorov" test), 309-314 (two-sample "Smirnov" test).
Shao, Jun and Dongsheng Tu. 1995. The Jackknife and Bootstrap. New York: Springer-Verlag.
Also see Match
, GenMatch
, balanceMV
,
balanceUV
, ks.boot
,
GerberGreenImai
, lalonde
# # Replication of Dehejia and Wahba psid3 model # # Dehejia, Rajeev and Sadek Wahba. 1999.``Causal Effects in Non-Experimental Studies: Re-Evaluating the # Evaluation of Training Programs.''Journal of the American Statistical Association 94 (448): 1053-1062. # data(lalonde) # # Estimate the propensity model # glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75, family=binomial, data=lalonde) # #save data objects # X <- glm1$fitted Y <- lalonde$re78 Tr <- lalonde$treat # # one-to-one matching with replacement (the "M=1" option). # Estimating the treatment effect on the treated (the "estimand" option which defaults to 0). # rr <- Match(Y=Y,Tr=Tr,X=X,M=1); #Let's summarize the output summary(rr) # # Let's check for balance # 'nboots' and 'nmc' are set to small values in the interest of speed. # Please increase to at least 500 each for publication quality p-values. mb <- MatchBalance(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75, data=lalonde, match.out=rr, nboots=10, nmc=10)