關於這本電子書
Causality is a key part of many fields and facets of life, from finding the relationship between diet and disease to discovering the reason for a particular stock market crash. Despite centuries of work in philosophy and decades of computational research, automated inference and explanation remains an open problem. In particular, the timing and complexity of relationships has been largely ignored even though this information is critically important for prediction, explanation and intervention. However, given the growing availability of large observational datasets including those from electronic health records and social networks, it is a practical necessity. This book presents a new approach to inference (finding relationships from a set of data) and explanation (assessing why a particular event occurred), addressing both the timing and complexity of relationships. The practical use of the method developed is illustrated through theoretical and experimental case studies, demonstrating its feasibility and success.
關於作者
Samantha Kleinberg is Assistant Professor of Computer Science at Stevens Institute of Technology. She received a Ph.D. in Computer Science and a B.A. in Computer Science and Physics from New York University.
為這本電子書評分
請分享你的寶貴意見。
閱讀資訊
智能手機和平板電腦
手提電腦和電腦
你可以使用電腦的網絡瀏覽器聆聽在 Google Play 上購買的有聲書。
電子書閱讀器及其他裝置
如要在 Kobo 等電子墨水裝置上閱覽書籍，你需要下載檔案並傳輸到你的裝置。請按照說明中心的詳細指示，將檔案傳輸到支援的電子書閱讀器。
