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Found 4 papers, 2 papers with code
Machine learning reveals how personalized climate communication can both succeed and backfire
Different advertising messages work for different people.
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects.
CausalML: Python Package for Causal Machine Learning
CausalML is a Python implementation of algorithms related to causal inference and machine learning.
Uplift Modeling for Multiple Treatments with Cost Optimization
An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously.
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