The Art of Causal Conjecture

From Artificial Intelligence Series

The Art of Causal Conjecture

By Glenn Shafer





In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.

The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences—medicine, business, engineering, and artificial intelligence—must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences.

Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.


Out of Print ISBN: 9780262193689 531 pp. | 8.1 in x 9.1 in


  • Probabilists and statisticians have long treated causality as an irritating diversion, lacking shape or substance. Shafer brilliantly reverses this view; he develops probability theory from its more natural, causal roots, and demonstrates the clumsiness of the traditional sample-space approach. This book will be of value to anyone thinking about foundational issues and will lend support to the growing movement of researchers, currently struggling to restore causality to its legitimacy in the sciences.

    Judea Pearl

    Professor, University of California

  • The 'probability tree' is a seemingly elementary device, first introduced by Huygens in 1676 to organise probability calculations. In Glenn Shafer's hands it becomes the key to a refreshing new approach to probability and causality. This is a work bursting with original ideas and results, which deserves the close attention of any one who cares about the meaning and applications of Probability and Statistics.

    A Philip Dawid

    Professor of Statistics, University College London

  • Causality plays an important role in many fields, from engineering to medicine to artificial intelligence. Glenn Shafer has written an important, scholarly study of causality. He starts with a novel foundation for probability that is mathematically and philosophically solid, uses it to distinguish carefully between notions of causality—all of which have played an important role in the literature—and shows how each can be discovered from evidence. This is a book that will be of interest to those interested in foundational questions of statistics and philosophy, as well as in practical applications of causality.

    Joseph Y. Halpern

    Professor, Computer Science Department, Cornell University