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  3. Causality, Probability, and Time
  • Cited by 25
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    • Publisher:
      Cambridge University Press
      Publication date:
      05 December 2012
      12 November 2012
      ISBN:
      9781139207799
      9781107026483
      9781107686014
      DOI:
      https://doi.org/10.1017/CBO9781139207799
      Dimensions:
      (234 x 156 mm)
      Weight & Pages:
      0.49kg, 265 Pages
      Dimensions:
      (234 x 156 mm)
      Weight & Pages:
      0.44kg, 267 Pages
      • Subjects:
      Philosophy of Science, Computer Science, Philosophy, Artificial Intelligence and Natural Language Processing
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    • Selected: Digital
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    Subjects:
    Philosophy of Science, Computer Science, Philosophy, Artificial Intelligence and Natural Language Processing
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    Book description

    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.

    Reviews

    '… informative and engaging … Arguably an equally valuable contribution of the book is its integration of relevant work in philosophy, computer science, and statistics.'

    David R. Bickel Source: Mathematical Reviews

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