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KDD 2024Workshop Summaries

2nd Workshop on Causal Inference and Machine Learning in Practice

Jeong-Yoon Lee, Yifeng Wu, Totte Harinen, Jing Pan, Paul Lo, Zhenyu Zhao, Huigang Chen, Zeyu Zheng 0002, Hasta Vanchinathan, Yingfei Wang, Roland Stevenson

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3637528.3671483 ↗

摘要

The workshop's rationale stems from the escalating interest in causal inference and machine learning methodologies within various industrial contexts. This surge in demand underscores the importance for both scholars and practitioners to exchange knowledge and best practices regarding the application of these techniques to tackle real-world challenges. Yet, applying causal machine learning techniques in real-world scenarios presents a range of challenges not addressed in the academic literature. This workshop aims to address the challenges for practical causal machine learning and explore new industry use cases. The workshop will provide a forum for practitioners and researchers to exchange ideas and explore new collaborations. Moreover, this workshop aims to capitalize on the success and achievements of the KDD 2023 Workshop titled "Causal Inference and Machine Learning in Practice".