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st: re: St : matching estimator

From   "Ariel Linden, DrPH" <>
To   <>
Subject   st: re: St : matching estimator
Date   Thu, 29 Dec 2011 11:58:02 -0500

Hi Ayman,

It is not clear from your post exactly what you are asking for, or trying to
do with your data? 

The propensity score is typically used as the matching variable when you
have a very large number of variables. You would find the appropriate
approach that minimizes the distance of the propensity score within matches.
Note that just because you have close matches on the propensity score, does
not ensure you have balance on the underlying covariates. Thus, it is more
important that you check for covariate balance than have balance on the
propensity score.

If you prefer to match on the covariates themselves, you have several
options. The most ubiquitous approach is mahalanobis distance matching. This
can be done within an existing user written program such as -psmatch2- or
-optmatch2-, or - mahapick-.

You could also consider -cem- which is another user written program that
uses stratification to bucket units.

My personal preference is to use a propensity score weighting approach
(IPTW, ATT, or ATE weights), and then model the outcome using that weight.
You could also consider kernel weighting, which is one of the options within

So basically you have a lot of options, but if you'd like a more specific
response, you'll have to ask your question in a more specific manner... 

I hope this helps


Date: Wed, 28 Dec 2011 12:22:58 -0800
From: Ayman Farahat <>
Subject: st: St : matching estimator

I am looking into using nmatch to evaluate treatment effect.
However, i am not sure if there is a natural meteric to find closest match.
Could the choice of meteric impact résult?
Any references to work comparing matching estimator to propensity or Heckman
greatly appreciated.
Sent from my iPad

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