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.