causalml package: can the random forest handle continuous response variable?

Question

There is a package for Python called causalml which can be used for uplift modeling. I'm trying to model the uplift when the response variable is continuous. The decision tree module in this package does not work on a continuous response variable, but it does when I use the random forest module. I saw somewhere in the documentation that tree-based modules in this package are only designed for classification problems. I was wondering if this package can handle continuous response variables at all. Can I trust the results I'm getting from the random forest module?

Answer 1

Based on the following answer uplift forest algorithms don't currently support continuous target variables.