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From |
Marcello Pagano <[email protected]> |

To |
<[email protected]> |

Subject |
Re: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables? |

Date |
Sat, 14 Jul 2012 12:33:12 -0400 |

We can, Peter. If the Listers want me to, I shall. You can also vote with your feet; ignore the postings. Do not respond. m.p. On 7/14/2012 12:22 PM, Lachenbruch, Peter wrote:

I am also frustrated by anonymous posters. Can we simple block such posts from appearing? Marcello? ________________________________________ From: [email protected] [[email protected]] On Behalf Of Steve Samuels [[email protected]] Sent: Saturday, July 14, 2012 6:28 AM To: [email protected] Subject: Re: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables? Please take note of the FAQ section: • "It is long-standing practice on Statalist that most members, especially the most active members who supply a large fraction of the answers, post using their real names. This is one of the ways in which we show respect to others. So we discourage you from posting from behind fake names or identifiers. Such handles are particularly objectionable if they include the word “Stata” in some way.. " I would add that "real name" means first and last name. Steve [email protected] On Jul 14, 2012, at 1:51 AM, TA Stat wrote: Thanks everyone for advice. I am figuring out how to collapse some categories of each variable in a meaningful way for my research question. I will keep my eyes on additional advice from everyone. Pete On Fri, Jul 13, 2012 at 10:12 PM, Austin Nichols <[email protected]> wrote:Ariel and Pete-- Estimating a logit with dummies is one way to combine across distinct combinations of the 15 observables to estimate a propensity score. A fully nonparametric propensity score would include every possible interaction as well, or simply compute the mean of treatment across all cells (possibly millions of cells). If any cells have pscore 0 or 1, and some are almost certain to be degenerate in that way, then you must combine that cell with another; one way of doing that is using the marginal across some subset of categories. The logit with no interactions is one particular method of combining across cells. sysuse auto logit foreign i.rep78 predict p if e(sample) egen m=mean(foreign), by(rep78) su m p if p<. * Note that if you do not restrict using if e(sample) * the estimated p=.818 for rep78=1 * (taken from excl cat rep78=5) when it should be zero. ta rep78, mi sum(foreign) ta rep78, mi sum(m) ta rep78, mi sum(p) g fakecat=round(mpg,10) logit foreign i.rep78##i.fakecat predict p2 if e(sample) egen m2=mean(foreign), by(rep78 fakecat) su m2 p2 if p2<. On Fri, Jul 13, 2012 at 10:19 AM, Ariel Linden, DrPH <[email protected]> wrote:Hi Pete, Since estimation of the propensity score is nothing more than a logistic (or probit) regression model, you could leave the categorical variables as-is and use the "i." prefix to denote that they are categorical, such as i.race. The regression output will show you that the levels of the categorical variable have been dealt with accordingly (including if any of the levels are dropped from the model). See for example: sysuse auto logit foreign i.rep78 On the other hand, you could certainly create dummy variables for the categorical variable. However, if you have a large number of covariates, your dataset will start looking ugly in a hurry. In any case, your results will be identical: tab rep78, gen(rep78_) logit foreign rep78_1- rep78_5 I hope this helps Ariel Date: Fri, 13 Jul 2012 10:06:14 +0700 From: TA Stat <[email protected]> Subject: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables? Dear All In PS matching, I am wondering about how to handle multiple categorical variables e.g. 15 variables. Each variable has multiple categories e.g. 3-5 categories. Do I have to create dummy variables, (n-1 for each variable), for all those categorical variables before calculating propensity score? Thanks Pete* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re:st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables?***From:*"Ariel Linden, DrPH" <[email protected]>

**Re: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables?***From:*Austin Nichols <[email protected]>

**Re: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables?***From:*TA Stat <[email protected]>

*From:*Steve Samuels <[email protected]>

**RE: st: Propensity Score Matching with Multiple Categorical Variables with Multiple Categories...Dummy Variables?***From:*"Lachenbruch, Peter" <[email protected]>

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