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

 From To Subject SV: st: Imbalance in control versus treated group, and weights Date Thu, 9 Oct 2008 23:37:29 +0200

```Austin, thanks a lot for the clarification! I missed the point about a discontinuity jump coming from a continuous variable, though the word itself should have pointed me towards that! Also, I should have been able to work it out for myself following the examples. Nothing wrong with the examples, they are excellent!

And while I am at it. Thanks to Statalist! I am a fan and a daily lurker, a great way of learning new things, and in the modern times of e-mail friendly cellphones one may learn a Stata trick or two even on the buss on my way home :)

Thanks.

-----Opprinnelig melding-----
Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne av Austin Nichols
Sendt: 9. oktober 2008 23:16
Til: statalist@hsphsun2.harvard.edu
Emne: Re: st: Imbalance in control versus treated group, and weights

Note that there are references in the help for -rd- (ssc inst rd,
replace) which mention specific examples and details on assumptions.
According to your first post, you have a dummy that increased Pr(treatment), not a discontinuous jump in Pr(treatment) at some cutoff in a continuous var, so no RD seems possible, and you expect your dummy to have a direct impact on outcomes as well, so no Instrumental Variables either.  It's unclear whether a matching/reweighting approach would work for you.

On Thu, Oct 9, 2008 at 4:56 PM,  <Alexander.Severinsen@telenor.com> wrote:
> I have another question. I followed the advice and looked into
> propensity score reweighting (PSR) and regression discontinuity (RD).
> 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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