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 <[email protected]> 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.
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
