Hi,
I've trying to utilise Matchit and zelig together to produce a conditional
ATT quantity. Published examples show how to do this when the parametric
model coefficient estimates are obtained from zelig from running the model
just over the control units (with suitable weights obtained from Matchit).
My control sample is really too small to accurately estimate these
coefficients. My adapted code is below, which runs without errors, but
produces an estimate that (I think) are too far near zero and statistically
insignificant. It's certainly much smaller than the relevant highly
statistically significant treatment coefficient in the parametric model.
I've a feeling the actual ATT estimate I'm producing is the average
difference between the actual dependent outcome value and its predicted
value in the treated group. Possibly the "sim" command doesn't realise the
treatment/control binary indicator needs to be set to 0 in the data matrix
input from the "setx" output to estimate the counterfactual (not sure from
reading help file)? Any help is much appreciated - especially suggested code
changes. Many thanks from an R and zelig novice.
In code below "ocular_nasal" is my treatment/control binary indicator
variable with 1 = treatment; and 0 = control.
k.match.out<- matchit(nasal_ocular ~ m_age + m_bmi + smoke, data = k.gsk,
distance = "mahalanobis", method = "genetic", discard =
"both", ratio =
1,pop.size=400, int.seed=3313)
k.z.out <- zelig(RQ_Act ~ nasal_ocular + m_age + m_bmi + smoke, model =
"ls", data = match.data(k.match.out))
k.x.out <- setx(k.z.out,fn = NULL, data = match.data(k.match.out, "treat"),
cond = TRUE)
set.seed(3251)
k.s.out <- sim(k.z.out, x=k.x.out, num=1000)
summary(k.s.out)
Many thanks,
Stephen
Stephen Kay ¦ Director of Statistics
for Adelphi Real World (Manchester)
Adelphi Mill, Bollington, Cheshire SK10 5JB UK
Web:
http://www.adelphigroup.com/companies/company_real_world.asp
<http://www.adelphigroup.com/companies/company_real_world.asp>
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