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
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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
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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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