The library implements modern causal ML methods including uplift trees and meta-learners that estimate individual treatment effects without strong parametric assumptions. It provides scikit-learn compatible APIs for model training, prediction, and evaluation. Typical applications include campaign targeting optimization, personalized interventions, and A/B test analysis where the goal is identifying which users will respond favorably to a specific action.