Martin,
Thank you very much! I should be able to afford that $7.50
Best wishes,
Alexander
-----Opprinnelig melding-----
Fra: [email protected] [mailto:[email protected]] På vegne av Martin Weiss
Sendt: 9. oktober 2008 22:59
Til: [email protected]
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: <[email protected]>
To: <[email protected]>
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: [email protected]
> [mailto:[email protected]] På vegne av
> [email protected]
> Sendt: 8. oktober 2008 19:11
> Til: [email protected]
> 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: [email protected]
> [mailto:[email protected]] På vegne av Austin
> Nichols
> Sendt: 8. oktober 2008 18:39
> Til: [email protected]
> 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, <[email protected]>
> 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
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