I'm using the "Match" package to do propensity score matching. Here's some example code that shows the problem that I'm having (much of this code is taken from the Match package documentation):
*data(lalonde) 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) X <- glm1$fitted Y <- lalonde$re78 Tr <- lalonde$treat # one-to-one matching with replacement (the "M=1" option). # Estimating the treatment effect on the treated (the "estimand" option defaults to ATT). rr <- Match(Y=Y, Tr=Tr, X=X, M=1);* And here's where the 'problem' occurs: *summary(rr) # gives an estimate of 2153.3 mean(rr$mdata$Y[rr$index.treated])-mean(rr$mdata$Y[rr$index.control]) # gives an estimate of 1083.848 * Notice that when I simply subtract the means from one another, I get a different estimate (1083.848) than when the algorithm outputs (2153.3). It seems that the obvious answer is that I'm not computing the estimate properly. If so, how is it computed? One more related question. I'm actually trying to do propensity score matching to estimate the effect of treatment on a dichotomous variable. Would the function change at all if the estimated effect is on a dichotomous scale? -- Dustin Fife PhD Student Quantitative Psychology University of Oklahoma [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.