Computation, Causation, and Discovery
by Clark Glymour and Gregory F. Cooper (eds.)
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.
http://mitpress.mit.edu/book-home.tcl?isbn=0262571242