46 citations · 82 across the 5 of their papers we have counts for
5 papers
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Zhenyu Zhao, Yumin Zhang, Totte Harinen +1
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a…
CausalML: Python Package for Causal Machine Learning
Huigang Chen, Totte Harinen, Jeong-Yoon Lee +2
CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending…
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Zhenyu Zhao, Radhika Anand, Mallory Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature se…
Uplift Modeling for Multiple Treatments with Cost Optimization
Zhenyu Zhao, Totte Harinen
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of in…
Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test and Adaptive Experimental Design
Zhenyu Zhao, Mandie Liu, Anirban Deb
During the rapid development cycle for Internet products (websites and mobile apps), new features are developed and rolled out to users constantly. Features with code defects or de…