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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. Preface / Clark Glymour -- An Overview Of The Representation And Discovery Of Causal Relationships Using Bayesian Networks / Gregory F. Cooper -- Causation, Representation And Prediction -- Prediction And Experimental Design With Graphical Causal Models / Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, Stephen Fienberg, Elizabeth Slate -- Graphs, Structural Models, And Causality / Judea Pearl -- Search -- A Bayesian Approach To Causal Discovery / David Heckerman, Christopher Meek, Gregory F. Cooper -- Truth Is Among The Best Explanations: Finding Causal Explanations Of Conditional Independence And Dependence / Richard Scheines, Clark Glymour, Peter Spirtes, Christopher Meek, Thomas Richardson -- An Algorithm For Causal Inference In The Presence Of Latent Variables And Selection Bias / Peter Spirtes, Christopher Meek, Thomas Richardson -- Automated Discovery Of Linear Feedback Models / Thomas Richardson, Peter Spirtes -- Controversy Over Search -- On The Impossibility Of Inferring Causation From Association Without Background Knowledge / James M. Robins, Larry Wasserman -- On The Possibility Of Inferring Causation From Association Without Background Knowledge / Clark Glymour, Peter Spirtes, Thomas Richardson -- Rejoinder To Glymour, Spirtes, And Richardson / James M. Robins, Larry Wasserman -- Response To Rejoinder / Clark Glymour, Peter Spirtes, Thomas Richardson -- Estimating Causal Effects. Edited By Clark Glymour And Gregory F. Cooper. Includes Bibliographical References And Index. 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 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.