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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
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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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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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