Michael Mayer wants to suss out the effects of inputs into a causal forest model:
We use a causal forest [1] to model the treatment effect in a randomized controlled clinical trial. Then, we explain this black-box model with usual explainability tools. These will reveal segments where the treatment works better or worse, just like a forest plot, but multivariately.
Read on for the example, as well as several mechanisms you can use to gauge feature relevance.