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Re: st: Propensity Score Matching Between 3 Groups


From   Austin Nichols <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: Propensity Score Matching Between 3 Groups
Date   Wed, 26 Feb 2014 16:54:54 -0500

Isobel Williams <[email protected]>:

The practical implementation of Fernando's first suggestion depends on
your data, but if you have exogenous treatment predictors in the local
`x' and a treatment dummy t, plus a variable group with value labels
1="A", 2="B", 3="C" then you can:

logit t `x' if inlist(group,1,2)
predict double p if inlist(group,1,3)
psmatch2 t, p(p) out(y) `options'

But I am unclear on why you would want to do this, as there is no
guarantee that this type of matching will produce appropriate balance,
even in expectation, much less in practice.

On Wed, Feb 26, 2014 at 2:58 PM, Fernando Rios Avila <[email protected]> wrote:
> Hi Isobel,
> So here is what I know about this.
> If what you want to do is to indeed apply the propensity scores from
> the A vs B group for the A vs C group, I would run the logit between A
> and B, and then predict the propensity score for all three groups.
> Once the propensity score is estimated, you can indicate within the
> -psmatch2- the specific propensity score you want to use, instead of
> having it estimate a separately logit model.
> The other alternative, given that there is nothing that would indicate
> that people in group B are equal to people in group C, is to estimate
> the propensity score using a multinomial logit for the three groups,
> and then proceed with your analysis with each pair group of interest.
> (for example C vs B with B as treated group) (C vs A) and (B vs A)
> Hope this helps.
> Fernando
>
>
> On Wed, Feb 26, 2014 at 2:01 PM, Isobel Williams
> <[email protected]> wrote:
>> Hi,
>>
>> I am trying to estimate a propensity score and then find the
>> average treatment effect on the treated. However, my sample has 3
>> groups:
>>
>> Group A: Resides in intervention area, receives treatment
>> Group B: Resides in intervention area, doesn't receive treatment
>> Group C: Resides in non-intervention area, doesn't receive treatment
>>
>> Thisis effectively like having 1 intervention group and 2 control groups: I
>>  want to calculate a propensity score for treatment between Groups A and
>>  B. I then want to apply this propensity score to Groups A and C to find
>>  the average treatment effect on the treated.
>>
>> I have seen this done in several economic papers, but never explained thoroughly. Is it
>> possible to do this in Stata, and if so, how?
>>
>> If it is relevant, I am using a logit model and -psmatch2- (the user-written SSC programme) to estimate the propensity score.
>>
>> Thanks!
>> Isobel Williams
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