R help - Apr 2005 - exclusion rules for propensity score matchng (pattern rec)

Dear R-list,

i have 6 different sets of samples.  Each sample has about 5000 observations,
with each observation comprised of 150 baseline covariates (X), 125 of which
are dichotomous. Roughly 20% of the observations in each sample are
"treatment"
and the rest are "control" units.

i am doing propensity score matching, i have already estimated propensity
scores(predicted probabilities) using logistic regression, and in each sample i
am going to have to exclude approximately 100 treated observations for which I
cannot find matching control observations (because the scores for these treated
units are outside the support of the scores for control units).

in each sample, i must identify an exclusion rule that is interpretable on the
scale of the X's that excludes these unmatchable treated observations and
excludes as FEW of the remaining treated observations as possible.
(the reason is that i want to be able to explain, in terms of the Xs, who the
individuals are that I making causal inference about.)

i've tried some simple stuff over the past few days and nothing's
worked.
is there an R-package or algorithm, or even estimation strategy that anyone
could recommend?
(i am really hoping so!)

thank you,

alexis diamond

adiamond at fas.harvard.edu wrote:
> Dear R-list,
> 
> i have 6 different sets of samples.  Each sample has about 5000
observations,
> with each observation comprised of 150 baseline covariates (X), 125 of
which
> are dichotomous. Roughly 20% of the observations in each sample are
"treatment"
> and the rest are "control" units.
> 
> i am doing propensity score matching, i have already estimated propensity
> scores(predicted probabilities) using logistic regression, and in each
sample i
> am going to have to exclude approximately 100 treated observations for
which I
> cannot find matching control observations (because the scores for these
treated
> units are outside the support of the scores for control units).
> 
> in each sample, i must identify an exclusion rule that is interpretable on
the
> scale of the X's that excludes these unmatchable treated observations
and
> excludes as FEW of the remaining treated observations as possible.
> (the reason is that i want to be able to explain, in terms of the Xs, who
the
> individuals are that I making causal inference about.)
> 
> i've tried some simple stuff over the past few days and nothing's
worked.
> is there an R-package or algorithm, or even estimation strategy that anyone
> could recommend?
> (i am really hoping so!)
> 
> thank you,
> 
> alexis diamond
> 
Exclusion can be based on the non-overlap regions from the propensity. 
It should not be done in the individual covariate space.  I tend to look 
at the 10th smallest and largest values of propensity for each of the 
two treatment groups for making the decision.  You will need to exclude 
non-overlap regions whether you use matching or covariate adjustment of 
propensity but covariate adjustment (using e.g. regression splines in 
the logit of propensity) is often a better approach once you've been 
careful about non-overlap.

Frank Harrell
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University