Hi,

I posted the following on the R discussion group, but this might be relevant 

here as well. 

I am trying 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). This is my source:

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. I use the more recent R version (2.12.1) and have updated 

the Zelig and MatchIt libraries. 

> update.packages("MatchIt")

> update.packages("Zelig") 

> 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