[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
<Alexander.Severinsen@telenor.com> |

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

Subject |
SV: st: Imbalance in control versus treated group, and weights |

Date |
Thu, 9 Oct 2008 23:11:27 +0200 |

Martin, Thank you very much! I should be able to afford that $7.50 Best wishes, Alexander -----Opprinnelig melding----- Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne av Martin Weiss Sendt: 9. oktober 2008 22:59 Til: statalist@hsphsun2.harvard.edu Emne: Re: st: Imbalance in control versus treated group, and weights http://www.stata-journal.com/article.html?article=st0136 HTH Martin _______________________ ----- Original Message ----- From: <Alexander.Severinsen@telenor.com> To: <statalist@hsphsun2.harvard.edu> Sent: Thursday, October 09, 2008 10:56 PM Subject: SV: st: Imbalance in control versus treated group, and weights >I have another question. I followed the advice and looked into >propensity score reweighting (PSR) and regression discontinuity (RD). >Google pointed me to Austins presentation about this topis, >http://www.stata.com/meeting/6nasug/causal.pdf > > I have read through the presentation, but I do not understand all the > assumptions that underpins RD. My problem pass the first assumption > that my treatment is not randomly assigned, though it started out as a > randomized controlled trial, just that not all those supposed to have > a treatment got one. Further, the assignment variable is based on a > observable variable. Or well, it was not supposed to be an assignment > variable, but it turned out to be, and consequently contaminated the > treated versus the control group. > > However I am uncertain what the second assignment is telling me, > quoting Austins presentation > > "The crucial second assumption is that there is a discontinuity at > some cutoff value of the assignment variable in the level of treatment." > > My assignment variable do produce a jump in the level of treatment, > but I am unsure whether this actually means that I pass assumption 2? > > I also downloaded the RD package from SSC (findit regression > discontinuity). However, I am still unclear how I can relate the > provided example to my own problem. I am having trouble locating other > examples, and any tip would be greatly appreciated. > > Best wishes, > Alexander Severinsen > > > -----Opprinnelig melding----- > Fra: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne av > Alexander.Severinsen@telenor.com > Sendt: 8. oktober 2008 19:11 > Til: statalist@hsphsun2.harvard.edu > Emne: SV: st: Imbalance in control versus treated group, and weights > > Thank you for the advice. Very helpful! > > In this spesific case z is a dummy, and if z=1 then this will increase > the likelihood of observing x=1. And yes, I do observe outcomes for > the group that was supposed to be treated, but were not. > > Best wishes, > Alexander > > -----Opprinnelig melding----- > Fra: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne av Austin > Nichols > Sendt: 8. oktober 2008 18:39 > Til: statalist@hsphsun2.harvard.edu > Emne: Re: st: Imbalance in control versus treated group, and weights > > It is possible that some kind of propensity score reweighting or > regression discontinuity design would be appropriate here, but without > much more information, it is hard to offer any specific advice. How > does z affect x in the group supposed to have x=1? Do you observe > outcomes for the group supposed to have x=1 but having x=0? Etc. > > Running a probit with the assumption E(y)=F(b0+b1*x+b2*z) seems > unlikely to recover a good estimate of the effect of x on y unless > that assumption is actually true! > > On Wed, Oct 8, 2008 at 12:23 PM, <Alexander.Severinsen@telenor.com> > wrote: >> Dear Statalisters, >> >> I have the following problem. I have given a sample of 10000 people >> as targets for receiving an offer, and I have a control group equal >> to 5000 people. I know that the potentially treated and the >> controlgroup is representative. However, without my knowledge only >> 8000 of the 10000 targets were treated, and a specific criteria was >> used to pick those 8000 from the 10000. >> >> This has created an imbalance between my controlgroup and those >> treated, and this imbalance is identified and only concerns one >> variable. I want to investigate whether the offer given could reduce >> the defection rate of customers, but the variable that created this >> imbalance is known to hugely impact the defection rate. To reduce >> this problem I would like to use weights in Stata, but I am unsure on >> how to approach this? Any tips would be greatly appreciated. >> >> Also, say that I did not correct for this, and did the following >> probit model with the following variables, y=defected/not defected, >> x=treated/control, z=factor that created imbalance: >> y=b0+b1*x+b2*z >> would it be appropriate to say that it was possible to control for >> the imbalance by including it as a independent variable in this fashion? >> >> Best wishes, >> Alexander Severinsen > * > * 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/

**Follow-Ups**:**Re: st: Imbalance in control versus treated group, and weights***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**References**:**st: Imbalance in control versus treated group, and weights***From:*<Alexander.Severinsen@telenor.com>

**Re: st: Imbalance in control versus treated group, and weights***From:*"Austin Nichols" <austinnichols@gmail.com>

**SV: st: Imbalance in control versus treated group, and weights***From:*<Alexander.Severinsen@telenor.com>

**SV: st: Imbalance in control versus treated group, and weights***From:*<Alexander.Severinsen@telenor.com>

**Re: st: Imbalance in control versus treated group, and weights***From:*"Martin Weiss" <martin.weiss1@gmx.de>

- Prev by Date:
**Re: st: Imbalance in control versus treated group, and weights** - Next by Date:
**Re: st: Imbalance in control versus treated group, and weights** - Previous by thread:
**Re: st: Imbalance in control versus treated group, and weights** - Next by thread:
**Re: st: Imbalance in control versus treated group, and weights** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |