Papers, proceedings and conferences
- Harinen, T., Filipowicz, A., Hakimi, S., Iliev, R., Klenk, M. and Sumner, E. (2022). Machine learning reveals how personalized climate communication can both succeed and backfire. NeurIPS Workshop on Causal Machine Learning for Real-World Impact
- Harinen T, Iliev R, Mueller S, Li A, Zhang C. Combining experimental and observational studies to estimate individual treatment effects: applications to customer journey optimization. 1st Workshop on End-End Customer Journey Optimization. KDD 2022.
- Zhao Z and Harinen T. CausalML: A Python Package for Uplift Modeling and Causal Inference Empowered by Machine Learning Methods. Causal Inference and Machine Learning in Practice: Challenges Across Industry. JSM 2022.
- Yanxia Zhang, Francine Chen, Shabnam Hakimi, Totte Harinen, Alex Filipowicz, Yan-Ying Chen, Rumen Iliev, Nikos Arechiga, Kalani Murakami, Kent Lyons, Charlene Wu and Matt Klenk. ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning. M-PREF. IJCAI 2022.
- Filipowicz AL, Wu CC, Lee ML, Shamma DA, Hakimi S, Carter S, Iliev R, Harinen T, Sumner E, Hogan C. Familiarity plays a unique role in increasing preferences for battery electric vehicle adoption. In Proceedings of the Annual Meeting of the Cognitive Science Society 2022 (Vol. 44, No. 44).
- Zhao Z, Zhang Y, Harinen T, Yung M. “Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effects.” IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, Cham, 2022.
- Syrgkanis, V., Lewis, G., Oprescu, M., Hei, M., Battocchi, K., Dillon, E., Pan, J., Wu, Y., Lo, P., Chen, H. and Harinen, T., 2021, August. Causal inference and machine learning in practice with econml and causalml: Industrial use cases at microsoft, tripadvisor, uber. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 4072-4073).
- CausalML: A Python package for uplift modeling and causal inference with machine learning. Causal Data Science Meeting 2021
- CausalML: A Python Package for Uplift Modeling and Causal Inference with Machine Learning. MIT Code 2021
- Panel: Causal ML in Industry. Microsoft Research Summit 2021.
- Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber. Lecture-Style Tutorial. KDD 2021.
- Z. Zhao and T. Harinen, “Uplift Modeling for Multiple Treatments with Cost Optimization,” 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), * Washington, DC, USA, 2019, pp. 422-431.
- Harinen, T. Normal Causes for Normal Effects: Reinvigorating the Correspondence Hypothesis About Judgments of Actual Causation. Erkenn 82, 1299–1320 (2017).
- Harinen, T. (2018). Mutual manipulability and causal inbetweenness. Synthese, 195(1), 35-54.
Preprints and white papers