46 citations · 58 across the 5 of their papers we have counts for
5 papers
ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning
Yanxia Zhang, Francine Chen, Shabnam Hakimi +9
Understanding consumer preferences is essential to product design and predicting market response to these new products. Choice-based conjoint analysis is widely used to model user…
Machine learning reveals how personalized climate communication can both succeed and backfire
Totte Harinen, Alexandre Filipowicz, Shabnam Hakimi +3
Different advertising messages work for different people. Machine learning can be an effective way to personalise climate communications. In this paper we use machine learning to r…
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…
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…