ks.boot: Bootstrap Kolmogorov-Smirnov
In Matching: Multivariate and Propensity Score Matching with Balance Optimization

ks.boot

R Documentation

Bootstrap Kolmogorov-Smirnov

Description

This function executes a bootstrap version of the univariate Kolmogorov-Smirnov test which provides correct coverage even when the distributions being compared are not entirely continuous. Ties are allowed with this test unlike the traditional Kolmogorov-Smirnov test.

Usage

ks.boot(Tr, Co, nboots=1000, alternative = c("two.sided","less","greater"),
        print.level=0)

Arguments

`Tr`	A vector containing the treatment observations.
`Co`	A vector containing the control observations.
`nboots`	The number of bootstraps to be performed. These are, in fact, really Monte Carlo simulations which are preformed in order to determine the proper p-value from the empiric.
`alternative`	indicates the alternative hypothesis and must be one of '"two.sided"' (default), '"less"', or '"greater"'. You can specify just the initial letter. See `ks.test` for details.
`print.level`	If this is greater than 1, then the simulation count is printed out while the simulations are being done.

Value

`ks.boot.pvalue`	The bootstrap p-value of the Kolmogorov-Smirnov test for the hypothesis that the probability densities for both the treated and control groups are the same.
`ks`	Return object from `ks.test`.
`nboots`	The number of bootstraps which were completed.

Author(s)

Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, https://www.jsekhon.com.

References

Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.” Journal of Statistical Software 42(7): 1-52. \Sexpr[results=rd]{tools:::Rd_expr_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. https://www.jsekhon.com

Abadie, Alberto. 2002. “Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models.” Journal of the American Statistical Association, 97:457 (March) 284-292.

Examples

#
# 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);
summary(rr)

#
# Do we have balance on 1975 income after matching?
#
ks  <- ks.boot(lalonde$re75[rr$index.treated], lalonde$re75[rr$index.control], nboots=500)
summary(ks)

Matching documentation built on Oct. 14, 2024, 5:09 p.m.