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 > 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: > [EMAIL PROTECTED] 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 > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html