| Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform Z Zhao, R Anand, M Wang 2019 IEEE international conference on data science and advanced analytics …, 2019 | 189 | 2019 |
| Causalml: Python package for causal machine learning H Chen, T Harinen, JY Lee, M Yung, Z Zhao arXiv preprint arXiv:2002.11631, 2020 | 103 | 2020 |
| Uplift modeling for multiple treatments with cost optimization Z Zhao, T Harinen 2019 IEEE International Conference on Data Science and Advanced Analytics …, 2019 | 48 | 2019 |
| Online experimentation diagnosis and troubleshooting beyond aa validation Z Zhao, M Chen, D Matheson, M Stone 2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016 | 28 | 2016 |
| Online product testing using bucket tests Z Zhao, FTP Oliveira, M Stone, M Chen, S Pandey, K Tripathi US Patent App. 14/509,741, 2016 | 12 | 2016 |
| Integrated likelihood computation methods Z Zhao, TA Severini Computational Statistics 32 (1), 281-313, 2017 | 10 | 2017 |
| Feature selection methods for uplift modeling Z Zhao, Y Zhang, T Harinen, M Yung Computer Science, Mathematics (May 2020). https://arxiv. org/abs, 2005 | 10* | 2005 |
| Inform product change through experimentation with data-driven behavioral segmentation Z Zhao, Y He, M Chen 2017 IEEE International Conference on Data Science and Advanced Analytics …, 2017 | 7 | 2017 |
| Safely and quickly deploying new features with a staged rollout framework using sequential test and adaptive experimental design Z Zhao, M Liu, A Deb 2018 3rd International Conference on Computational Intelligence and …, 2018 | 5 | 2018 |
| Causalml: Python package for causal machine learning, 2020 H Chen, T Harinen, JY Lee, M Yung, Z Zhao Cited on, 14, 2002 | 5 | 2002 |
| Causalml: Python package for causal machine learning. arXiv 2020 H Chen, T Harinen, J Lee, M Yung, Z Zhao arXiv preprint arXiv:2002.11631, 0 | 4 | |
| 2nd Workshop on End-End Customer Journey Optimization H Zhao, Z Zhao, A Xu, N Gupta, M Bay Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | | 2023 |
| Causal Inference and Machine Learning in Practice: Use Cases for Product, Brand, Policy and Beyond JY Lee, Y Wu, K Battocchi, F Vera, Z Zhao, T Harinen, J Pan, H Chen, ... Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | | 2023 |
| 1st Workshop on End-End Customer Journey Optimization N Gupta, Z Zhao, M Bay, A Xu, F Farooq Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | | 2022 |
| Staged rollout framework for feature release Z Zhao, M Liu, A Deb, A Parikh, S Mehta US Patent 11,226,804, 2022 | | 2022 |
| Integrated Likelihood Computation Methods and Application Z Zhao Northwestern University, 2014 | | 2014 |
| The Weather Impact on Travel Behavior in Chicagoland Z Zhao CMAP: Chicago Metropolitan Agency for Planning, 2012 | | 2012 |
| causal-machine-learning/kdd2021-tutorial Goto Github PK LZ Link | | |
| Package ‘IntLik’ Z Zhao, MZ Zhao | | |
