From the data, you cannot tell whether there exists an omitted variable. Also,
coefficients and standard errors of the propensity score model do not mean much.
Propensity score is a tool to achieve covariate balance.
Kosuke
Department of Politics
Princeton University
Thank You very much Kosuke,
I will check my variables right now.
May I ask you a little methodological question ?
When I use the (logit) propensity matching method , the average propensity score for
treated is about 12%...
because only a small part of the treated has scores between 50% and
80%...
Does it means that there is an omitted variable somewhere ? Or there is no absolute
reference level and I have to assess this figure relatively to the average score of the
control group?
I was expecting to obtain a propensity score much higher for the treated ... also
because when I run the same logistic specification of the probability of bein treated
using another stat package (in order to see the significance of the coefficients) I obtain
that all my variables are highly significant at the 1% level...
What do you think ?
Many thanks
On 27 February 2012 04:36, Kosuke Imai <kimai(a)princeton.edu> wrote:
> My guess is that there is a perfect collinearity among your variables.
>
> Kosuke
>
> Department of Politics
> Princeton University
>
http://imai.princeton.edu
>
> On Feb 24, 2012, at 5:32 PM, Francesco wrote:
>
>> Dear Matchit list,
>>
>> I am using matchit for my work, and I really appreciate the excellent work you
have done so far.
>> I have a question : I perform a nearest neighbor matching procedure with a large
dataset ( 40 000 individuals, 15 variables) and when I use the standard propensity score
as a distance, everything works fine : the matching is quite good
>> However if I specify the "mahalanobis" distance I get an error saying
that :
>>
>> "Lapack dgesv : le système est exactement singulier" (the system is
exactly singular)...
>>
>> Do you have an idea of what might cause this problem ? I have no missing data...
>>
>> Many thanks
>> -
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