re: st: Propensity Score Matching

["Ariel Linden. DrPH" <ariel.linden@gmail.com>]

This sounds to me like a case of mixing apples and oranges.

In its most general form, propensity score matching is intended to balance
observed baseline covariates between treated and non-treated individuals in
order to "replicate" an RCT (ie., that at least on observed characteristics
individuals are exchangeable). 

Here you are matching at the aggregate level, and then you want to make
statements about treatment effects at the individual level? In considering
my last sentence, would you argue that you can achieve balance on individual
covariates when you are matching at the aggregate cross-sectional level? If
not, why would you think that your outcomes (measured at the individual
level) would not be potentially biased?

It seems to me that you may want to consider a difference-in-differences
model using the cross-sectional data as covariates and outcome. If you want
to use a PS approach (or any "matching" approach for that matter), you'll
need to have individual level characteristics and outcomes. You could throw
in some cross-sectional covariates if they make sense, depending on your
content expertise.

Whatever you do, it needs to stand up to scientific scrutiny, and I am not
sure you can make the case as you posed it.

I hope this helps

Ariel



Date: Sat, 16 Jun 2012 01:12:26 -0700 (PDT)
From: niken kusumawardhani <koesoemawardhanie@yahoo.com>
Subject: st: Propensity Score Matching

Hi all,

I have a question on Propensity Score Matching. I'm trying to evaluate the
impact of migration on children's schooling. My data is cross-section and I
do not have child-level data at time before migration occured. But I have
data on household-level at time before migration occured. Therefore, I
decided to match based on household-level data, since it is measured before
participation into migration.

Since my outcome is at individual-level, there might be some individual
characteristics that affect my outcome. Estimating the impact of migration
by propensity score matching constructed based on household-level variables
won't be enough. My question is, can I estimate the impact of migration
using propensity score matching (covariates used are household-level) and
also incorporate some individual-level variables?

I'm thinking of estimating such model:

Sij = Mj + Gj + Aij + Bij + e

For Sij = schooling of child i at household j
Mj = 1 for migrant household, 0 for household without migrant
Gj = propensity score for household j (the same for all kids at one
household)
Aij = for example age of the child i
Bij = for example sex of the child i
and then since children in household are related, I'm gonna cluster the
standard errors at household level.

Is that possible to do this with Propensity Score Matching? Could someone
tell me how to do it in Stata? 

I've read a lot of references using PSM, but none of it has additional
variables to predict the ATT like in my problems.



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