balanceUV: Univariate Balance Tests

Description

This function provides a number of univariate balance metrics. Generally, users should call MatchBalance and not this function directly.

Usage

balanceUV(Tr, Co, weights = rep(1, length(Co)), exact = FALSE, ks=FALSE,
          nboots = 1000, paired=TRUE, match=FALSE,
          weights.Tr=rep(1,length(Tr)), weights.Co=rep(1,length(Co)),
          estimand="ATT")

Arguments

A vector containing the treatment observations.

A vector containing the control observations.

weights

A vector containing the observation specific weights. Only use this option when the treatment and control observations are paired (as they are after matching).

exact

A logical flag indicating if the exact Wilcoxon test should be used instead of the test with a correction. See wilcox.test for details.

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 ev

nboots

The number of bootstrap samples to be run for the ks test. If zero, no bootstraps are done. Bootstrapping is highly recommended because the bootstrapped Kolmogorov-Smirnov test only provides correct coverage even for non-continu

paired

A flag for if the paired t.test should be used.

match

A flag for if the Tr and Co objects are the result of a call to Match.

weights.Tr

A vector of weights for the treated observations.

weights.Co

A vector of weights for the control observations.

estimand

This determines if the standardized mean difference returned by the sdiff object is standardized by the variance of the treatment observations (which is done if the estimand is either "ATE" or "ATT") or by the variance of the cont

Value

sdiffThis is the standardized difference between the treated and control units multiplied by 100. That is, 100 times the mean difference between treatment and control units divided by the standard deviation of the treatment observations alone if the estimand is either ATT or ATE. The variance of the control observations are used if the estimand is ATC.
sdiff.pooledThis is the standardized difference between the treated and control units multiplied by 100 using the pooled variance. That is, 100 times the mean difference between treatment and control units divided by the pooled standard deviation as in Rosenbaum and Rubin (1985).
mean.TrThe mean of the treatment group.
mean.CoThe mean of the control group.
var.TrThe variance of the treatment group.
var.CoThe variance of the control group.
p.valueThe p-value from the two-sided weighted t.test.
var.ratiovar.Tr/var.Co.
ksThe object returned by ks.boot.
ttThe object returned by two-sided weighted t.test.
qqsummaryThe return object from a call to qqstats with standardization---i.e., balance test based on the empirical CDF.
qqsummary.rawThe return object from a call to qqstats without standardization--i.e., balance tests based on the empirical QQ-plot which retain the scale of the variable.

References

Sekhon, Jasjeet S. 2007. ``Multivariate and Propensity Score Matching Software with Automated Balance Optimization.'' Journal of Statistical Software. http://sekhon.berkeley.edu/papers/MatchingJSS.pdf 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

Rosenbaum, Paul R. and Donald B. Rubin. 1985. ``Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score.'' The American Statistician 39:1 33-38.

Hollander, Myles and Douglas A. Wolfe. 1973. Nonparametric statistical inference. New York: John Wiley & Sons.

Examples

Run this code

data(lalonde)
attach(lalonde)

foo  <- balanceUV(re75[treat==1],re75[treat!=1])
summary(foo)

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