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RE: st: Methods for selection bias


From   "Hema Mistry" <Hema.Mistry@brunel.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Methods for selection bias
Date   Tue, 25 Apr 2006 09:04:24 +0100

Thanks Rafa that is very helpful.

Hema

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of R.E. De Hoyos
Sent: 21 April 2006 19:30
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Methods for selection bias


If you are interested in selection bias, you cannot avoid reading Heckman:

 Heckman, J. (1979) `Sample selection bias as a
specification error', Econometrica, vol. 47, 1.

 Heckman, J. (1990) `Varieties of selection bias',
The American Economic Review, vol. 80, 2.

Carneiro, Hansen and Heckman (2003), `Estimating...', International
Economic Review, May, Vol. 44, No.2

Rafa

----- Original Message ----- 
From: "Anders Alexandersson" <andersalex@gmail.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Friday, April 21, 2006 2:45 PM
Subject: Re: st: Methods for selection bias


>I would consider -permute- for permutation tests based on Monte Carlo
> simulations. What assumptions can you make, e.g., do you have
> independent samples (i.e. unmatched data)?
>
> Anders Alexandersson
> andersalex@gmail.com
>
> Privately, Hema replied:
>> The "non-randomised data" refers to both the sample and the treatment 
>> assignment.
>
> On 4/20/06, Anders Alexandersson <andersalex@gmail.com> wrote:
>> Does "non-randomised data" here refer to the sample and/or to the
>> treatment assignment?
>>
>> On 4/20/06, Hema Mistry <Hema.Mistry@brunel.ac.uk> wrote:
>>
>> > I was wondering whether you can provide me with some advice or point me 
>> > in the right
>> > direction.  I am trying to find methods which can deal with data that 
>> > is non-randomised
>> > and suffers from selection bias. After searching various databases etc 
>> > I have come up
>> > with the following methods:
>> > 1) Regression analyses
>> > 2) Propensity score - matching, stratification, regression, 
>> > classification trees
>> > 3) Instrumental variables
>> > 4) Sample selection models
>> > 5) Two-part models
>> > 6) Inverse probability weighting
>> >
>> > Before I start using these methods in various datasets I was just 
>> > wondering whether
>> > users are aware of any other methods which I have not identified?
>> > Can you recommend any good text books or key people that maybe I should 
>> > contact?
>
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