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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