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 - 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 of "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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Descrizione libro AAAI Press, 1999. Paperback. Condizione libro: New. Codice libro della libreria P110262571242
Descrizione libro AAAI Press, 1999. Paperback. Condizione libro: New. book. Codice libro della libreria 262571242