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From |
"Ariel Linden" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: Re: st: Propensity Score Matching Between 3 Groups |

Date |
Thu, 27 Feb 2014 09:56:49 -0500 |

This is a good thought-provoking thread. Let me add in here a few thoughts: First, in the case of multiple control groups, it makes the most sense to treat them as separate treatment conditions. Thus, Fernando's second proposed methodology is the most suitable. That is, estimate the multinomial logit, with the probability of being in each of the three groups as the propensity score. I'll take it a bit further now and suggest that rather than matching, calculate the inverse-probability of treatment weights (IPTW) for each individual, based on their actual treatment "assignment" and on their estimated propensity score (taken from -mlogit-). Then you can use these weights within the context of an outcome regression model (speaking to Adam's last point). Lucky for Stata users, in version 13.0 it appears that all the approaches in -teffects- allow for multiple treatment groups. "The treatment model can be binary, or it can be multinomial, allowing for multivalued treatments." While I have used the various regressions adjustment models with multiple treatment arms and can attest to their "robustness", I have not played around with the -teffects psmatch- for this exercise. I hope this helps Ariel Date: Wed, 26 Feb 2014 17:15:49 -0500 From: Adam Olszewski <adam.olszewski@gmail.com> Subject: Re: st: Propensity Score Matching Between 3 Groups It may be worth noting however, that this procedure violates the principles of causal inference. If Group C resides in a non-intervention area, then their probability of receiving "treatment" is zero, and the positivity assumption required by propensity score analysis is not met. Perhaps this is somehow irrelevant to the study subject, but if causal inference assumptions are not met, then why not just use regular regression? AO On Wed, Feb 26, 2014 at 4:54 PM, Austin Nichols <austinnichols@gmail.com> wrote: > Isobel Williams <iwilliams24@hotmail.com>: > > The practical implementation of Fernando's first suggestion depends on > your data, but if you have exogenous treatment predictors in the local > `x' and a treatment dummy t, plus a variable group with value labels > 1="A", 2="B", 3="C" then you can: > > logit t `x' if inlist(group,1,2) > predict double p if inlist(group,1,3) > psmatch2 t, p(p) out(y) `options' > > But I am unclear on why you would want to do this, as there is no > guarantee that this type of matching will produce appropriate balance, > even in expectation, much less in practice. > > On Wed, Feb 26, 2014 at 2:58 PM, Fernando Rios Avila <f.rios.a@gmail.com> wrote: >> Hi Isobel, >> So here is what I know about this. >> If what you want to do is to indeed apply the propensity scores from >> the A vs B group for the A vs C group, I would run the logit between A >> and B, and then predict the propensity score for all three groups. >> Once the propensity score is estimated, you can indicate within the >> -psmatch2- the specific propensity score you want to use, instead of >> having it estimate a separately logit model. >> The other alternative, given that there is nothing that would indicate >> that people in group B are equal to people in group C, is to estimate >> the propensity score using a multinomial logit for the three groups, >> and then proceed with your analysis with each pair group of interest. >> (for example C vs B with B as treated group) (C vs A) and (B vs A) >> Hope this helps. >> Fernando * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Propensity Score Matching Between 3 Groups***From:*Alfonso Sánchez-Peñalver <alfonso.statalist@gmail.com>

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