The very essence of lasso is that it is supposed to select only one of two correlated variables.
However, when I include two highly correlated predictors (they are correlated with each other at level ~0.95), both of them are being selected with similar absolute coefficient value (on standardized predictor), but with different signs. This means their effect on prediction almost cancels out, but the coefficients from model on standardized input are highest of all variables.
x coefs (Intercept) 91.6958266 Population_2013 -49.2656083 Population_2014 46.8513210
where Variable1 and Variable2 are highly correlated. Other correlated and uncorrelated variables are also included in model. I run models on anything between 20 and 20000 variables and effect is similar for these correlated variables.
Is there any solution? Alternatively - how in any other way can I determine which variables affect significantly my prediction?