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From |
Cameron McIntosh <cnm100@hotmail.com> |

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

Subject |
st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy? |

Date |
Mon, 13 Aug 2012 07:05:01 -0400 |

And more directly related to the GPC method that Tirthankar suggested, I would recommend: Moodie, E.E.M., & Stephens, D.A. (2012). Estimation of dose–response functions for longitudinal data using the generalised propensity score. Statistical Methods in Medical Research, 21(2), 149-166. Cam ---------------------------------------- > From: cnm00@@hotmail.com > To: statalist@hsphsun..harvard.edu > Subject: RE: st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy? > Date: Sun, 2 Aug 012 2::5::8 -400< > > The following may also be of interest: > > Crown, W.H. (010)). There's a reason they call them dummy variables: a note on the use of structural equation techniques in comparative effectiveness research. Pharmacoeconomics, 8((0)), 47--55.. > > Cam > > > Date: Sun, 2 Aug 012 5::0::9 -700< > > Subject: Re: st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy? > > From: tirthankar.lists@gmail.com > > To: statalist@hsphsun..harvard.edu > > > > "Second, if I do not find another way than to break the treatment > > variable D into 0 dummies, does anyone know how I could recover the > > mean ATT and its standard error? I guess I would need to weight the 0< > > different ATTs that I got, but what should be the weights? How about > > number of treated observations in each treatment group?" > > > > The process you want is described in Imbens' 000 Biometrika paper > > which proposes the Generalised Propensity Score [GPS] > > dx.doi.org/0..093//biomet/7....06< > > See page 08.. > > > > T > > > > On Sun, Aug 2,, 012 at ::6 PM, John Carey <johncarey96@@gmail.com> wrote: > > > Hi everyone! > > > > > > I have been working on a difference-in-differences strategy, and I was > > > hoping someone could clarify an important point for me. > > > > > > In the beginning, the treatment I am working on was not a dummy. It is > > > a discrete variable ("D") which ranges from to 0 when observations > > > are treated, and equals otherwise. For the sake of simplicity, I > > > turned it into a dummy, equal to when the discrete variable is > > > strictly positive, and equal to otherwise. That way, I was able to > > > use a few common diff-in-diff models (OLS regression and psmatch)). > > > Also, I should specify that I only have periods (pre-treatment, and > > > post-treatment). > > > > > > However, I have been doing research about how to account for treatment > > > intensity, because I would like to take into account the fact that > > > being treated with 0 is not the same as being treated with .. > > > > > > For now, I have created 0 dummies for each of the possible values of > > > the treatment variable, and I have run 0 different regressions (< > > > against ;; against ;; against ....). However, it is not easy to > > > get a full picture with that process. First, I have very few treated > > > observations for some of the treatment values, and therefore inference > > > is an issue. Second, I have not found an easy way to compare the > > > treatment effects to each other, since I have compared each of them to > > > getting unit of treatment. > > > > > > Therefore, here are two questions ;) > > > > > > First, do you know of any way to account for treatment intensity > > > without breaking the treatment variable into 0 dummies? Ideally, I > > > would like to be able to run one regression which would take it all > > > into account. Some sort of weighted ATT. > > > For instance, do you think it is possible to use a regular OLS > > > diff-in-diff equation, plug the treatment variable as a discrete > > > variable (as opposed to a dummy), and include as many group fixed > > > effects and there are treatment values? I would be tempted to write it > > > like this: > > > Yit = a + b[T=t]] + c[[D=]] + c[[D=]] + ... + c0[[D=0]] + d[T=t]]*[D] + e > > > In that equaltion, I would let [D] range from to 0,, and d would be > > > the ATT. Do you think that makes sense? > > > > > > Second, if I do not find another way than to break the treatment > > > variable D into 0 dummies, does anyone know how I could recover the > > > mean ATT and its standard error? I guess I would need to weight the 0< > > > different ATTs that I got, but what should be the weights? How about > > > number of treated observations in each treatment group? I thought > > > about doing that, but I stopped because the fact that treatment was > > > not randomly allocated made me think otherwise. > > > > > > Thank you everyone for your help, and I wish you a great week! > > > * > > > * 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**:**st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy?***From:*John Carey <johncarey196@gmail.com>

**Re: st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy?***From:*Tirthankar Chakravarty <tirthankar.lists@gmail.com>

**RE: st: Diff-in-diff: how to account for treatment intensity, rather than just a treatment dummy?***From:*Cameron McIntosh <cnm100@hotmail.com>

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