Matching (version 3.3-3)

Matchby: Grouped Multivariate and Propensity Score Matching

Description

This function is a wrapper for the Match function which separates the matching problem into subgroups defined by a factor. This is equivalent to conducting exact matching on each factor. Matches within each factor are found as determined by the usual matching options. This function is much faster for large datasets than the Match function itself.

Usage

Matchby(Y, Tr, X, by, estimand = "ATT", M = 1, exact = NULL, caliper = NULL,
        Weight = 1, Weight.matrix = NULL, tolerance = 1e-05,
        distance.tolerance = 1e-05, print.level=1, version="fast", ...)

Arguments

Y

A vector containing the outcome of interest. Missing values are not allowed.

Tr

A vector indicating the observations which are in the treatment regime and those which are not. This can either be a logical vector or a real vector where 0 denotes control and 1 denotes treatment.

X

A matrix containing the variables we wish to match on. This matrix may contain the actual observed covariates or the propensity score or a combination of both.

by

A "factor" in the sense that as.factor(by) defines the grouping, or a list of such factors in which case their interaction is used for the grouping.

estimand

A character string for the estimand. The default estimand is "ATT", the sample average treatment effect for the treated. "ATE" is the sample average treatment effect (for all), and "ATC" is the sample average treatment effect for the controls

M

A scalar for the number of matches which should be found (with replacement). The default is one-to-one matching.

exact

A logical scalar or vector for whether exact matching should be done. If a logical scalar is provided, that logical value is applied to all covariates of X. If a logical vector is provided, a logical value should be provid

caliper

A scalar or vector denoting the caliper(s) which should be used when matching. A caliper is the distance which is acceptable for any match. Observations which are outside of the caliper are dropped. If a scalar caliper is provided, this cali

Weight

A scalar for the type of weighting scheme the matching algorithm should use when weighting each of the covariates in X. The default value of 1 denotes that weights are equal to the inverse of the variances. 2 denotes the Maha

Weight.matrix

This matrix denotes the weights the matching algorithm uses when weighting each of the covariates in X---see the Weight option. This square matrix should have as many columns as the number of columns of the X

tolerance

This is a scalar which is used to determine numerical tolerances. This option is used by numerical routines such as those used to determine if a matrix is singular.

distance.tolerance

This is a scalar which is used to determine if distances between two observations are different from zero. Values less than distance.tolerance are deemed to be equal to zero. This option can be used to perform a type of optimal

print.level

The level of printing. Set to '0' to turn off printing.

version

The version of the code to be used. The "fast" C/C++ version of the code does not calculate Abadie Imbens standard errors. The end-user should not change this option.

...

Additional arguments passed on to Match.

Value

• est
  The estimated average causal effect.
• se.standard
  The usual standard error. This is the standard error calculated on the matched data using the usual method of calculating the difference of means (between treated and control) weighted so that ties are taken into account.
• ret
  A matrix with three columns. The first column contains the outcomes for treated observations, the second the outcomes for control observations, and the third the weight given to each match.
• orig.nobs
  The original number of observations in the dataset.
• nobs
  The number of observations in the matched dataset.
• wnobs
  The number of weighted observations in the matched dataset.
• orig.treated.nobs
  The original number of treated observations.
• ndrops
  The number of matches which were dropped because there were not enough observations in a given group and because of caliper and exact matching.
• estimand
  The estimand which was estimated.
• version
  The version of Match which was used.

Details

Matchby is much faster for large datasets than Match. But Matchby only implements a subset of the functionality of Match. For example, the restrict option cannot be used, Abadie-Imbens standard errors are not provided and bias adjustment cannot be requested. Matchby is a wrapper for the Match function which separates the matching problem into subgroups defined by a factor. This is the equivalent to doing exact matching on each factor, and the way in which matches are found within each factor is determined by the usual matching options.

References

Sekhon, Jasjeet S. 2006. ``Matching: Algorithms and Software for Multivariate and Propensity Score Matching with Balance Optimization via Genetic Search.'' http://sekhon.berkeley.edu/matching/

Sekhon, Jasjeet S. 2006. ``Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference.'' Working Paper. http://sekhon.berkeley.edu/papers/SekhonBalanceMetrics.pdf

Abadie, Alberto and Guido Imbens. 2005. ``Large Sample Properties of Matching Estimators for Average Treatment Effects.'' Econometrica 74(1): 235-267. http://ksghome.harvard.edu/~.aabadie.academic.ksg/sme.pdf

Diamond, Alexis and Jasjeet S. Sekhon. 2005. ``Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.'' Working Paper. http://sekhon.berkeley.edu/papers/GenMatch.pdf

Imbens, Guido. 2004. Matching Software for Matlab and Stata. http://elsa.berkeley.edu/~imbens/estimators.shtml

See Also

Also see Match, summary.Matchby, GenMatch, MatchBalance, balanceMV, balanceUV, qqstats, ks.boot, GerberGreenImai, lalonde

Examples

#
# Match exactly by racial groups and then match using the propensity score within racial groups
#

data(lalonde)

#
# Estimate the Propensity Score
#
glm1  <- glm(treat~age + I(age^2) + educ + I(educ^2) + 
             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) after exactly
# matching on race using the 'by' option.  Estimating the treatment
# effect on the treated (the "estimand" option defaults to ATT).
rr  <- Matchby(Y=Y, Tr=Tr, X=X, by=lalonde$black, M=1);
summary(rr)