Computation, Causation, and Discovery

Overview

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.

Table of Contents

  1. Preface

    Clark Glymour

  2. Acknowledgments

    Clark Glymour and Gregory F. Cooper

  3. 1. An Overview of the Representation and Discovery of Causal Relationships Using Bayesian Networks

    Gregory F. Cooper

  4. Part One: Causation, Representation and Prediction
  5. 2. Prediction and Experimental Design with Graphical Causal Models

    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, Stephen Fienberg and Elizabeth Slate

  6. 3. Graphs, Structural Models, and Causality

    Judea Pearl

  7. Part Two: Search
  8. 4. A Bayesian Approach to Causal Discovery

    David Heckerman, Christopher Meek and Gregory F. Cooper

  9. 5. Truth is among the Best Explanations: Finding Causal Explanations of Conditional Independence and Dependence

    Richard Scheines, Clark Glymour, Peter Spirtes, Christopher Meek and Thomas Richardson

  10. 6. An Algorithm for Causal Inference in the Presence of Latent Variables and Selection Bias

    Peter Spirtes, Christopher Meek and Thomas Richardson

  11. 7. Automated Discovery of Linear Feedback Models

    Thomas Richardson and Peter Spirtes

  12. Part Three: Controversy Over Search
  13. 8. On the Impossibility of Inferring Causation from Association without Background Knowledge

    James M. Robins and Larry Wasserman

  14. 9. On the Possibility of Inferring Causation from Association without Background Knowledge

    Clark Glymour, Peter Spirtes and Thomas Richardson

  15. 10. Rejoinder to Glymour, Spirtes, and Richardson

    James M. Robins and Larry Wasserman

  16. 11. Response to Rejoinder

    Clark Glymour, Peter Spirtes and Thomas Richardson

  17. Part Four: Estimating Causal Effects
  18. 12. Testing and Estimation of Direct Effects by Reparameterizing Directed Acyclic Graphs with Structural Nested Models

    James M. Robins

  19. 13. A Clinician's Tool for Analyzing Noncompliance

    David Maxwell Chickering and Judea Pearl

  20. 14. Estimating Latent Causal: Influences: TETRAD II Model Selection and Bayesian Parameter Estimation

    Richard Scheines

  21. Part Five: Applications
  22. 15. Exploring Hypothesis Space: Examples from Organismal Biology

    Bill Shipley

  23. 16. In-Flight Calibration of Satellite Ion Composition Data Using Artificial Intelligence Methods

    Joakim Waldemark and Patrick Norqvist

  24. 17. Causal Modeling of Spectral Data: A New Tool to Study Nonlinear Processes

    Ludwik Liszka

  25. 18. Modeling Corn Exports and Exchange Rates with Directed Graphs and Statistical Loss Functions

    Derya G. Akleman, David A. Bessler and Diana M. Burton

  26. 19. Causal Inferences from Databases: Why Universities Lose Students

    Marek J. Druzdzel and Clark Glymour

  27. Index