Search Results for author: Totte Harinen

Found 4 papers, 2 papers with code

Feature Selection Methods for Uplift Modeling

1 code implementation5 May 2020 Zhenyu Zhao, Yumin Zhang, Totte Harinen, Mike Yung

To address this problem, we introduce a set of feature selection methods designed specifically for uplift modeling, including both filter methods and embedded methods.

feature selection

CausalML: Python Package for Causal Machine Learning

2 code implementations25 Feb 2020 Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao

CausalML is a Python implementation of algorithms related to causal inference and machine learning.

Causal Inference

Uplift Modeling for Multiple Treatments with Cost Optimization

no code implementations14 Aug 2019 Zhenyu Zhao, Totte Harinen

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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