[R] exclusion rules for propensity score matchng (pattern rec)

[Frank E Harrell Jr]

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