Open ncorream opened 3 years ago
It's possible to use a final model that enforces monotonicity (such as sklearn's Lasso
with positive=True
), but I'd be concerned that the fact that your results don't match your intuition might indicate that actually you have some unmeasured confounders that aren't being reflected in your first stage residuals.
Thank you I will try that. And yes, I have been thinking that I have some unknown confounders, because with a fake outcome variable dependent on treatment and confounders (in a positive trend) the model works just fine.
Hello again,
I have been trying to implement DML methods to predict the causal effect of a continuous treatment (from 0 to 300 aprox) and even if I got to the point of having a fairly good MSE score, almost half of the effects predicted are negative. I am an expert on the field from where the database is extracted, and it is impossible for this drug to have a negative effect. The effect is always positive, and increasing with higher quantities of treatment (up to a plateu in some patients).
My question is, is it possible to apply monotonicity constraints to the final model and in doing so specifying to the estimator that the treatment can only increase the effect?
Thank you in advance!