Machine Learning for Causal Inference (PDF)
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Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Dr. Zhixuan Chu is a researcher at Ant Group. He holds a Ph.D. in Biostatistics and a Master's degree in Computer Science from the University of Georgia, along with a Bachelor's degree in Statistics from Huazhong University of Science and Technology. His research pursuits are centered around trustworthy artificial intelligence and the various interdisciplinary applications it offers, with a particular focus on causal inference, striving to improve the effectiveness of causal inference with machine learning technologies and enhance the stability and interpretability of machine learning with causal inference technologies. He has published over 20 papers in top-tier computer science conferences and journals.
- 2023, 1st ed. 2023, 298 Seiten, Englisch
- Herausgegeben: Sheng Li, Zhixuan Chu
- Verlag: Springer International Publishing
- ISBN-10: 3031350510
- ISBN-13: 9783031350511
- Erscheinungsdatum: 25.11.2023
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- Dateiformat: PDF
- Größe: 11 MB
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