Dear R users,

I am trying to estimate to estimate the average treatmen effect on the
treated (ATT) using first the MatchIt software to weight the data set and,
after this, the Zelig software as shown in Ho et al. (2007). See here for an
explanation of how to apply this technique in R:

http://imai.princeton.edu/research/files/matchit.pdf

I encounter a slight problem when I apply the weights that are produced in
the stage of preprocessing the data. The idea of this is to use the MatchIt
software to preprocess the data and then use the Zelig software to generate
the distribution of ATT. I believe that the main reason for preprocessing
the data is to create weights (depending on the matching technique you use)
so that balance would be achieved for the matching variables between the
treatment and the control group. Then you use these weights in the
regressions that follow in the Zelig library. Copied from the matchit
article, whose link I provide above, the authors say:

"If one chooses options that allow matching with
replacement, or any solution that has different numbers of controls (or
treateds) within each
subclass or strata (such as full matching), then the parametric analysis
following matching
must accomodate these procedures, such as by using fixed effects or weights,
as appropriate.
(Similar procedures can also be used to estimate various other quantities of
interest such
as the average treatment effect by computing it for all observations, but
then one must
be aware that the quantity of interest may change during the matching
procedure as some
control units may be dropped.)"

The following code is for the "lalonde" data set, where I get an error
message in the end:

> library(Zelig)
> library(MatchIt)
> data(lalonde)
> m.out1 = matchit(treat ~ age + educ + black + hispan + nodegree + married
+ re74 + re75, method = "subclass", subclass=6, data = lalonde)
> z.out1 = zelig(re78 ~ age + educ + black + hispan + nodegree + married +
re74 + re75, data = match.data(m.out1, "control"), model = "ls",
weights="weights")
> x.out1 = setx(z.out1, data = match.data(m.out1, "treat"), cond = TRUE)
> s.out1 = sim(z.out1, x = x.out1)
Error in model.frame.default(formula = re78 ~ age + educ + black + hispan +
:
  variable lengths differ (found for '(weights)')

I was wondering if somebody could tell me how to get around with this
problem?

Also, I have seen people adding the propensity scores in the regression
analysis applied in the Zelig package, i.e.

> z.out1 = zelig(re78 ~ age + educ + black + hispan + nodegree + married +
re74 + re75 + *distance*, data = match.data(m.out1, "control"), model =
"ls", weights="weights")

Does anyone have a clue of why this can happen?

Kind regards,
Sotiris

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