Totte Harinen

According to our database, Totte Harinen authored at least 10 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Causal Inference and Machine Learning in Practice: Use Cases for Product, Brand, Policy and Beyond.

Jeong-Yoon Lee, Yifeng Wu, Keith Battocchi, Fabio Vera, Zhenyu Zhao, Totte Harinen, Jing Pan, Huigang Chen, Zeyu Zheng, Chu Wang, Yingfei Wang, Xinwei Ma

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Can Behavioral Experts Predict Outcome Heterogeneity?

Rumen Iliev, Alexandre L. S. Filipowicz, Emily Sarah Sumner, Francine Chen, Nikos Aréchiga, Scott A. Carter, Totte Harinen, Kate A. Sieck, Charlene C. Wu

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

2022

Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect.

Zhenyu Zhao, Yumin Zhang, Totte Harinen, Mike Yung

Proceedings of the Artificial Intelligence Applications and Innovations, 2022

Familiarity plays a unique role in increasing preferences for battery electric vehicle adoption.

Alexandre L. S. Filipowicz, Charlene C. Wu, Matthew L. Lee, David A. Shamma, Shabnam Hakimi, Scott A. Carter, Rumen Iliev, Totte Harinen, Emily Sumner, Candice Hogan

Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022

2021

Machine learning reveals how personalized climate communication can both succeed and backfire.

Totte Harinen, Alexandre L. S. Filipowicz, Shabnam Hakimi, Rumen Iliev, Matthew Klenk, Emily Sumner

CoRR, 2021

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber.

Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, Yifeng Wu, Paul Lo, Huigang Chen, Totte Harinen, Jeong-Yoon Lee

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Feature Selection Methods for Uplift Modeling.

Zhenyu Zhao, Yumin Zhang, Totte Harinen, Mike Yung

CoRR, 2020

CausalML: Python Package for Causal Machine Learning.

Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao

CoRR, 2020

2019

Uplift Modeling for Multiple Treatments with Cost Optimization.

Zhenyu Zhao, Totte Harinen

Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics, 2019

2018

Mutual manipulability and causal inbetweenness.

Totte Harinen

Synth., 2018