27 Mar
2011
27 Mar
'11
9:12 p.m.
As Gary explained, identification of treatment effect heterogeneity is a
problem that is somewhat separate from matching. I've been interested in
this topic for some time and have two papers which may interest you. In
my opinion, this is an important area of research but not much work has
been done yet.
http://imai.princeton.edu/research/choice.html
http://imai.princeton.edu/research/svm.html
Kosuke
--
Department of Politics
Princeton University
http://imai.princeton.edu
On Thu, 24 Mar 2011, Gary King wrote:
With matching this doesn't necessarily cause any
problems since the goal is
to estimate the average treatment effect (or the average treatment effect on
the treated or something like that). The general approach allows for that
variability. The classical regression approach, in contrast, often assumes
constant treatment effects, and so heterogeneity of effects can cause bias
or incorrectly estimated standard errors.
Gary
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IQSS - Harvard University
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@kinggary<http://twitter.com/kinggary>- 617-500-7570 - Asst 495-9271 -
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On Thu, Mar 24, 2011 at 2:55 PM, georgebaah <gtg498n(a)mail.gatech.edu> wrote:
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Hi Everyone,
Suppose each unit in the treatment group responds to the treatment
differently, how does this affect the causal estimate? In general, what
does it mean
to have heterogenous treatment effects? Thanks.
George
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