Re: st: Propensity score matching: Must all treated samples have a counterfactual?

[Ricky Lim <ricky.lim12345@gmail.com>]

Dear Ariel & Sebastian,

Thank you very much for your replies!
They were very helpful.

And thanks for the two articles, Ariel.
I have read one of them today and will read another one tomorrow.
And yes, I meant caliper, not radius.
Sorry for the confusion.

***

Dear Ariel, Sebastian & all Statalisters,

My study is about organisational merger actually.
So there are 2 organisations pre-treatment (pre-merger) and 1
organisation post-treatment (post-merger).

I'm generating the propensity scores based on the per-treatment observables.
The standard deviation for my PS is around 0.06.
20 – 25% of that would be 0.012 – 0.015.

I only have 20 organisations for 9 mergers.
Applying this caliper on my samples would mean that 5 organisations
(one each from a different merger, hence 5 mergers) will have no
counterfactuals.
This leaves me with only 4 mergers which are too small a sample.
I think if one of the constituent organisations do not have a
couunterfactuals pre-merger, I would have to give up the whole merger
case.

Also, each of the constituent organisation for the remaining 4 mergers
have 1 to >10 counterfactuals within the caliper +-0.015.
Is it necessary for each of the treated organisation to have the same
number of counterfactuals?
Can one of them have one, and another one have say, five?

At the moment,
I am more keen to just use 3 nearest neighbour without caliper, just
so that I can still use all 20 organisations / 9 mergers.
But that will involve the assumption that the matches are good enough.

Would be grateful to hear your thoughts / advices.

Thank you very much in advanced.

Regards,
Ricky

On 19 August 2013 08:35, Sebastian Beil (dienstlich)
<sebastian.beil@ruhr-uni-bochum.de> wrote:
> Hello Ricky,
>
> if you cannot match every treated observation your estimated treatment
> effect becomes a local one (it is bound to the matched sample). This is not
> very rare with propensity score matching.
>
> As regards the choice of the caliper/radius you should bear in mind, that
> matching serves the goal to balance background covariates. If that can be
> achieved with a certain caliper everything is fine. So checking the
> balancing property after matching is most important. On the other side, a
> caliper size of 0.5 seems far to large (if your pscore is the probability
> for receiving treatment) and part of your treated observations are of
> definitely support. In that case you should stick with the local average
> treatment effect for the treated (or reestimate the pscore with a different
> model).
>
> Best regards,
> Sebastian
>
> Am 17.08.2013 08:33, schrieb statalist-digest:
>
>> Date: Fri, 16 Aug 2013 14:57:09 +0100
>> From: Ricky Lim<ricky.lim12345@gmail.com>
>> Subject: st: Propensity score matching: Must all treated samples have a
>> counterfactual?
>>
>> Dear Statalisters,
>>
>> I ran running propensity score matching using -pscore- and would like
>> to do radius & nearest neighbour matching (nearest 3 within +- x
>> score).
>> 15 out of 20 of my treated samples have more than 3 counterfactuals
>> within 0.05 scores,
>> whereas the remaining 5 have 1 or none.
>> Their propensity scores are at the extreme and I would need to
>> increase the range to +-0.5 before they will have 3 counterfactuals
>> each.
>>
>> My questions are:
>> 1. Must every treated sample have a counterfactual / control?
>> 2. Is a radius of 0.5 score too wide? Is it be acceptable to use the
>> nearest 3 within 0.5 score?
>>
>> Any advice is deeply appreciated.
>>
>> Thank you very much in advanced.
>>
>> Regards,
>> Ricky
>
>
>
> --
> Sebastian Beil, M.A.
>
> Wissenschaftlicher Mitarbeiter
> Lehrstuhl für empirische Sozialforschung
>
> Ruhr-Universität Bochum
> Fakultät für Sozialwissenschaft
> Universitätsstr. 150
> D-44780 Bochum
>
> Raum: GB 1/32
> Tel.: +49 (0)234 32 - 27791
> e-mail: sebastian.beil@rub.de
>
>
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