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

From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>
Date: Wed 06 Apr 2005 - 05:42:41 EST

Alexis J. Diamond wrote:
> hi,
>
> thanks for the reply to my query about exclusion rules for propensity
> score matching.
>
>

>>Exclusion can be based on the non-overlap regions from the propensity.
>>It should not be done in the individual covariate space.

>
>
> i want a rule inspired by non-overlap in propensity score space, but that
> binds in the space of the Xs. because i don't really know how to
> interpret the fact that i've excluded, say, people with scores > .87,
> but i DO know what it means to say that i've excluded people from
> country XYZ over age Q because i can't find good matches for them. if i
> make my rule based on Xs, i know who i can and cannot make inference for,
> and i can explain to other people who are the units that i can and cannot
> make inference for.
>
> after posting to the list last night, i thought of using the RGENOUD
> package (genetic algorithm) to search over the space of exclusion rules
> (eg., var 1 = 1, var 2 = 0 var 3 = 1 or 0, var 4 = 0); the loss function
> associated with a rule should be increasing in # of tr units w/out support
> excluded and decreasing in # of tr units w/ support excluded.
>
> it might be tricky to get the right loss function, and i know this idea is
> kind of nutty, but it's the only automated search method i could think of.
>
> any comments?
>
> alexis

Use the X space directly will not result in optimum exclusions unless you use a distance function but that will make assumptions. My advice is to use rpart to make a classification rule that approximates the exclusion criteria to some desired degree of accuracy. I.e. use rpart to predict propensity < lower cutoff and separately to predict propensity > upper cutoff. This just assists in interpretation.

Frank

>
>
>

>>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

>
>
>
> On Tue, 5 Apr 2005, Frank E Harrell Jr wrote:
>
>
>>adiamond@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
>>>
>>
>>
>>
>>--
>>Frank E Harrell Jr   Professor and Chair           School of Medicine
>>                      Department of Biostatistics   Vanderbilt University
>>

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

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Received on Wed Apr 06 05:52:08 2005

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