Why is the mean of confidence interval the same as the coefficients in Logistic Regression? I'm currently using statsmodels library to train a Logistic Regression.
However, I noticed that logit.conf_int().mean(axis=1) and logit.param generates the same values. Why is that? What am I missing regarding confidence interval and coefficients relation?
I'm calling 'logit' the following model:
import statsmodels.formula.api as smf
logit = smf.logit(formula=function, data=df).fit()

I know I should share some data, but I don't think this is relevant in here, since you can achieve the same thing with any data you have available.

The conf_int() method returns confidence intervals for the coefficients.  GLM usually uses a normal approximation to the likelihood function, meaning the intervals are symmetric (hence the coefficient estimate is the mid point of the interval).
This in conjunction with the fact that the midpoint of an interval is the arithmetic mean of the endpoints should be enough to understand why.
