Computation, causation, and discovery / edited by Clark Glymour and Gregory F. Cooper.

Format:

Book

Contributor:

Cooper, Gregory Floyd, editor.

Glymour, Clark N., editor.

Language:

English

Subjects (All):

Causation.

Data mining.

Prediction theory.

Physical Description:

1 online resource (xv, 552 pages) : illustrations

Other Title:

Computation, causation & discovery

Place of Publication:

Cambridge, Massachusetts : The MIT Press, [1999]

Language Note:

English

Summary:

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.

Notes:

Includes index.

OCLC-licensed vendor bibliographic record.

ISBN:

0-262-31582-3

OCLC:

1014474586