Computation, Causation, and Discovery

Front Cover
Clark N. Glymour, Gregory Floyd Cooper
AAAI Press, 1999 - Computers - 552 pages
0 Reviews
In science, business, and policymaking—anywhere data are used in prediction—two sorts of problems requiring very different methods of analysis often arise. The first, problems of recognition and classification, concerns learning how to use some features of a system to accurately predict other features of that system. The second, problems of causal discovery, concerns learning how to predict those changes to some features of a system that will result if an intervention changes other features. This book is about the second—much more difficult—type of problem.

Typical problems of causal discovery are: How will a change in commission rates affect the total sales of a company? How will a reduction in cigarette smoking among older smokers affect their life expectancy? How will a change in the formula a college uses to award scholarships affect its dropout rate? These sorts of changes are interventions that directly alter some features of the system and perhaps—and this is the question—indirectly alter others.

The contributors discuss recent research and applications using Bayes nets or directed graphic representations, including representations of feedback or "recursive" systems. The book contains a thorough discussion of foundational issues, algorithms, proof techniques, and applications to economics, physics, biology, educational research, and other areas.

From inside the book

What people are saying - Write a review

We haven't found any reviews in the usual places.


Causation Representation and Prediction
Chapter Three

13 other sections not shown

Common terms and phrases

About the author (1999)

Clark Glymour is Senior Research Scientist at IHMC and Alumni University Professor of Philosophy at Carnegie Mellon University.

Cooper is Associate Professor of Medicine and of Intelligent Systems at the University of Pittsburgh, where he is also Senior Associate in the Center for Biomedical Informatics.

Bibliographic information