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wschmidt＠hbs.edu

14 Apr 2009 14 Apr '09

5:10 p.m.

Despite an earlier email chain on a related subject, the syntax to use matchit() with propensity scores rather than covariates is still not clear to me, if anyone can help. Do you set "distance=" to the propensity score vector? What do you put in the "formula" argument? Thanks!

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jpolley＠jd10.law.harvard.edu

14 Apr 14 Apr

6:01 p.m.

In my understanding, the "nearest" method in matchit() creates a propensity score using the covariates, and matches on that propensity score (the syntax of matchit() will look quite like what we were shown in Miya's section notes). When we use matchit(), the default that it uses to determine propensity scores is the logit model. The matchit() manual on gary's website details some other options that one can use in matchit() in place of the logit model, and are specified by <distance="nameofmodel">. The italicized phrase in the problem set seems only to be telling us not to use some other form of matching on the covariates without a propensity score. On Tue, Apr 14, 2009 at 5:10 PM, Schmidt, William <wschmidt at hbs.edu> wrote:

Despite an earlier email chain on a related subject, the syntax to use matchit() with propensity scores rather than covariates is still not clear to me, if anyone can help. Do you set ?distance=? to the propensity score vector? What do you put in the ?formula? argument? Thanks! _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

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woolfalk＠fas.harvard.edu

7:29 p.m.

You are on the right track in thinking that question 3 wants you to use nearest neighbor propensity score matching rather than some other nearest neighbor distance metric. However, the formula in your call to matchit() should not contain the 5 covariates. We want you to match on the propensity scores and not the covariates. In other words, we want you to estimate the propensity scores using a model specification of your choice before using matchit() and use these values to conduct the matching. Miya On Tue, Apr 14, 2009 at 6:01 PM, John Polley <jpolley at jd10.law.harvard.edu>wrote:

In my understanding, the "nearest" method in matchit() creates a propensity score using the covariates, and matches on that propensity score (the syntax of matchit() will look quite like what we were shown in Miya's section notes). When we use matchit(), the default that it uses to determine propensity scores is the logit model. The matchit() manual on gary's website details some other options that one can use in matchit() in place of the logit model, and are specified by <distance="nameofmodel">. The italicized phrase in the problem set seems only to be telling us not to use some other form of matching on the covariates without a propensity score. On Tue, Apr 14, 2009 at 5:10 PM, Schmidt, William <wschmidt at hbs.edu>wrote:

Despite an earlier email chain on a related subject, the syntax to use matchit() with propensity scores rather than covariates is still not clear to me, if anyone can help. Do you set ?distance=? to the propensity score vector? What do you put in the ?formula? argument? Thanks! _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

_______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

-- Miya Woolfalk Ph.D. Student Harvard University Government and Social Policy

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jpolley＠jd10.law.harvard.edu

8:43 p.m.

I understand the matchit() function (when using "nearest") to already be calculating a propensity score from the covariates, using the logit function to do so by default, and then matching based on that propensity score. If our call to matchit() (using "nearest") were to plug in the propensity scores themselves (rather than the 5 covariates), wouldn't we be asking matchit() to calculate a propensity-score-of-propensity-score, and to match based on that? As it turns out, it appears one reaches the same answer either way (at least in the problem set example). But substantively, I'm not clear on why we would want such a propensity-on-propensity approach. I have understood the usefulness of propensity scores to be in finding close matches where, with multiple covariates, we would have too few exact matches. Am I mistaking the terminology somewhere? There is material from Gary's site that refers to the matchit() function (using the default, "nearest") with a call including the covariates as "matching on the estimated propensity score." Or is it that you would like us to figure out a way to execute nearest neighbor propensity score matching without using the "nearest" function in MatchIt at all? On Tue, Apr 14, 2009 at 7:29 PM, Miya Woolfalk <woolfalk at fas.harvard.edu>wrote:

You are on the right track in thinking that question 3 wants you to use nearest neighbor propensity score matching rather than some other nearest neighbor distance metric. However, the formula in your call to matchit() should not contain the 5 covariates. We want you to match on the propensity scores and not the covariates. In other words, we want you to estimate the propensity scores using a model specification of your choice before using matchit() and use these values to conduct the matching. Miya On Tue, Apr 14, 2009 at 6:01 PM, John Polley <jpolley at jd10.law.harvard.edu

wrote:

In my understanding, the "nearest" method in matchit() creates a propensity score using the covariates, and matches on that propensity score (the syntax of matchit() will look quite like what we were shown in Miya's section notes). When we use matchit(), the default that it uses to determine propensity scores is the logit model. The matchit() manual on gary's website details some other options that one can use in matchit() in place of the logit model, and are specified by <distance="nameofmodel">. The italicized phrase in the problem set seems only to be telling us not to use some other form of matching on the covariates without a propensity score. On Tue, Apr 14, 2009 at 5:10 PM, Schmidt, William <wschmidt at hbs.edu>wrote:

Despite an earlier email chain on a related subject, the syntax to use matchit() with propensity scores rather than covariates is still not clear to me, if anyone can help. Do you set ?distance=? to the propensity score vector? What do you put in the ?formula? argument? Thanks! _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

_______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

-- Miya Woolfalk Ph.D. Student Harvard University Government and Social Policy _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

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woolfalk＠fas.harvard.edu

10:31 p.m.

see below. On Tue, Apr 14, 2009 at 8:43 PM, John Polley <jpolley at jd10.law.harvard.edu>wrote:

I understand the matchit() function (when using "nearest") to already be calculating a propensity score from the covariates, using the logit function to do so by default, and then matching based on that propensity score.

yes.

If our call to matchit() (using "nearest") were to plug in the propensity scores themselves (rather than the 5 covariates), wouldn't we be asking matchit() to calculate a propensity-score-of-propensity-score, and to match based on that?

yes, if you leave everything else at the defaults. alternatively, you could pass to the distance argument your vector of propensity scores.

As it turns out, it appears one reaches the same answer either way (at least in the problem set example). But substantively, I'm not clear on why we would want such a propensity-on-propensity approach. I have understood the usefulness of propensity scores to be in finding close matches where, with multiple covariates, we would have too few exact matches.

the point of the question is to make sure that everyone understands what propensity score matching is all about.

Am I mistaking the terminology somewhere? There is material from Gary's site that refers to the matchit() function (using the default, "nearest") with a call including the covariates as "matching on the estimated propensity score." Or is it that you would like us to figure out a way to execute nearest neighbor propensity score matching without using the "nearest" function in MatchIt at all? On Tue, Apr 14, 2009 at 7:29 PM, Miya Woolfalk <woolfalk at fas.harvard.edu>wrote:

You are on the right track in thinking that question 3 wants you to use nearest neighbor propensity score matching rather than some other nearest neighbor distance metric. However, the formula in your call to matchit() should not contain the 5 covariates. We want you to match on the propensity scores and not the covariates. In other words, we want you to estimate the propensity scores using a model specification of your choice before using matchit() and use these values to conduct the matching. Miya On Tue, Apr 14, 2009 at 6:01 PM, John Polley < jpolley at jd10.law.harvard.edu> wrote:

In my understanding, the "nearest" method in matchit() creates a propensity score using the covariates, and matches on that propensity score (the syntax of matchit() will look quite like what we were shown in Miya's section notes). When we use matchit(), the default that it uses to determine propensity scores is the logit model. The matchit() manual on gary's website details some other options that one can use in matchit() in place of the logit model, and are specified by <distance="nameofmodel">. The italicized phrase in the problem set seems only to be telling us not to use some other form of matching on the covariates without a propensity score. On Tue, Apr 14, 2009 at 5:10 PM, Schmidt, William <wschmidt at hbs.edu>wrote:

Despite an earlier email chain on a related subject, the syntax to use matchit() with propensity scores rather than covariates is still not clear to me, if anyone can help. Do you set ?distance=? to the propensity score vector? What do you put in the ?formula? argument? Thanks! _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

_______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

-- Miya Woolfalk Ph.D. Student Harvard University Government and Social Policy _______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

_______________________________________________ gov2001-l mailing list gov2001-l at mailman.fas.harvard.edu http://mailman.fas.harvard.edu/mailman/listinfo/gov2001-l

-- Miya Woolfalk Ph.D. Student Harvard University Government and Social Policy

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jpolley＠jd10.law.harvard.edu
woolfalk＠fas.harvard.edu
wschmidt＠hbs.edu