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Re: Re: st: Propensity scoring using -teffects- in Stata 13; puzzling features, documentation issues


From   "Lacy,Michael" <Michael.Lacy@colostate.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: Re: st: Propensity scoring using -teffects- in Stata 13; puzzling features, documentation issues
Date   Wed, 4 Sep 2013 17:09:41 +0000

Thanks to David Drukker for the prompt and thorough response, and for (in good StataCorps 
tradition) not taking a defensive posture regarding questions.  I'd like to push a bit on 
some of my remarks, though.

First, thank to Tom Weichle for in a followup lending some support re my questioning the 
enforcement of a matching with replacemement policy. It's good to know that someone 
more knowledgeable than I had similar thoughts.  One other thought I would have here 
is that if StataCorp does not want give  countenance to without replacement analyses, 
perhaps it could offer a "match cases only" option, without actually doing the analyses.  
My own motivation was to do a matched analysis not available in the -teffects-package. 
It might have been a bad idea, but I'd like to have the opportunity to do something bad 
now and then <grin>. I do understand that user-written packages like -psmatch2- offer 
some choices in similar functionality, but it's nice to have something from StataCorp itself.


David Drukker had written in response to my posting:

>From 	  "David M. Drukker" <ddrukker@stata.com>
>To 	  "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
>Subject 	  Re: st: Propensity scoring using -teffects- in Stata 13; puzzling features, documentation issues,
>Date 	  Tue, 3 Sep 2013 17:12:22 -0500 (CDT)
>
>Mike's second question is essentially, why do the implemented estimators
>include all the observations whose distances are tied. [referring here to 
controls matched to treated cases]

>discussion of ties is given in Abadie et al (2004, page 293).  There are two
>arguments for including all the tied observations.  First, following from
>the quote above, including all the ties provides a more precise estimator.

Several responses from me:

1) I had always understood that the gain in precision from multiple matches
in a matched analysis is not that great beyond 5 or so, and I think
this would be particularly true when matching with replacement is used,
so that (e.g.) my 100 matches may be the same as your 100 matches.
I don't know how the SE is calculated, though.

2) I hadn't realized (my fault) that the "excess" matches only occur
when there are ties in propensity scores of potential matches. Nevertheless, 
my experience  requesting 2 controls was that I thought something was wrong 
with the software, as doing the calculations with (in many cases) 100s of
 controls/treated case when I had requested only 2 apparently really bogged 
things down and confused me.  I thought I had done something wrong and that 
the program had hung. Perhaps a warning of some kind is in order?  

3) I presume there's nothing theoretically wrong with randomly choosing among tied
controls.  I'd give up some precision (well beyond all the inherent errors in my data)
to have a procedure that runs in seconds rather than minutes. Others might feel
differently, and I might feel differently at different times myself. 

Anyway, I'd still be interested in comments from others with deeper expertise
than I can claim.

Regards,


Mike Lacy
Dept. of Sociology
Colorado State University
Fort Collins CO 80523-1784

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