Matching: balanceUV – R documentation

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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

`Tr`	A vector containing the treatment observations.
`Co`	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.
`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.
`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-continuous covariates. At least 500 `nboots` (preferably 1000) are recommended for publication quality p-values.
`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 control observations (which is done if the estimand is "ATC").

Value

`sdiff`	This 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.pooled`	This 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.Tr`	The mean of the treatment group.
`mean.Co`	The mean of the control group.
`var.Tr`	The variance of the treatment group.
`var.Co`	The variance of the control group.
`p.value`	The p-value from the two-sided weighted `t.test`.
`var.ratio`	var.Tr/var.Co.
`ks`	The object returned by `ks.boot`.
`tt`	The object returned by two-sided weighted `t.test`.
`qqsummary`	The return object from a call to `qqstats` with standardization—i.e., balance test based on the empirical CDF.
`qqsummary.raw`	The 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.

Author(s)

Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.berkeley.edu/.

References

Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.” Journal of Statistical Software 42(7): 1-52. doi: 10.18637/jss.v042.i07

Diamond, Alexis and Jasjeet S. Sekhon. 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Review of Economics and Statistics. 95 (3): 932–945. http://sekhon.berkeley.edu/papers/GenMatch.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

data(lalonde)
attach(lalonde)

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

Matching

Multivariate and Propensity Score Matching with Balance Optimization

v4.10-2

GPL-3

Authors

Jasjeet Singh Sekhon [aut, cre], Theo Saarinen [aut]

Initial release

2022-04-13