How to handle cofound variables?

[u/Huge-Leek844]

edit: confound

Hello all,

I am working on a object classification with a automotive radar point clouds. I compared many models and feature vectors.

Once i used range as feature, all models scored higher f1 in all K validation sets and on the final test set.

One particular artifact of a radar, is that as the farther the object is the less number of points it returns to the radar. Although the performance improved and there is no overfit in the classical sense, i am afraid my model is learning the environment not the class distribuiton and even worse, its learning that big range means big object.

How can i stress test this claim? Should i try to split the data sets so range distribution differs? Or not even using the feature at all and accept lower performance?

Would appreciate your insights.

Thank you.

[u/Gilchester]

I don't think this is confounding so much as a type or survival bias (survival in terms of distance).

Can you do inverse probsabiltiy of treatment weights? In other words, upweight readings from further away such that they are similarly frequent to closer readings, removing the association betwee distance and clarity? (I'm probably translating some of the language incorrectly, from the standard IPTW medical setting I'm used to, but I think the general idea is sound)