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Re: st: constructing and implementing logistic regression


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: constructing and implementing logistic regression
Date   Fri, 5 Feb 2010 11:25:27 -0500

Maarten and Mike--
I missed the start of this thread, but it sounds like this might be helpful:
http://www.stata-journal.com/article.html?article=st0150

On Fri, Feb 5, 2010 at 3:14 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> --- On Thu, 4/2/10, Mike Smith wrote:
>> I am trying to do propensity score matching, but first need
>> to do logistic regression and that's what I am have trouble
>> with. suppose I have a model as follows: gpa (the dependent
>> variable) and sex and race being the independent variables.
> <snip>
>
> This suggests that you think you can make propensity weights
> by estimating a logit on a continuous variable. logistic
> regression is a model for a binary depedent variable, so this
> way you make propensity scores when your treatment variable
> is binary. Estimating a causal effects for continous
> explanatory variables is much harder. See for instance Stephen
> L. Morgan and Christopher Winship (2007) Counterfactuals and
> Causal Inference: Methods and Principles for Social Research.
> Cambridge University Press. I am not recomending that you
> turn gpa in a binary variable, in most cases that just doesn't
> make substantive sense. The book I refered to earlier does
> provide some options for continous explanatory variables.
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