Minds and Machines

, Volume 16, Issue 3, pp 289–302 | Cite as

The power of intervention

  • Kevin B. Korb
  • Erik Nyberg


We further develop the mathematical theory of causal interventions, extending earlier results of Korb, Twardy, Handfield, & Oppy, (2005) and Spirtes, Glymour, Scheines (2000). Some of the skepticism surrounding causal discovery has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (cf. Cartwright, (2001). Our results show that experimental data, together with some plausible assumptions, can reduce the space of viable explanatory causal models to one.


Causal models Causal discovery Faithfulness Simplicity Intervention Bayesian networks Underdetermination 



We are grateful for a Monash Research Fund grant which helped to support this work. We thank Charles Twardy, Lucas Hope, Rodney O’Donnell and anonymous referees for helpful comments on this paper.


  1. Cartwright, N. (2001). What is wrong with Bayes nets? The Monist, 84, 242–264.MathSciNetGoogle Scholar
  2. Chickering, D. M. (1995). A tranformational characterization of equivalent Bayesian net- work structures. In P. Besnard & S. Hanks (Eds.), 11th Conference on Uncertainty in AI (pp. 87–98). San Francisco.Google Scholar
  3. Hesslow, G. (1976). Discussion: Two notes on the probabilistic approach to causality. Philosophy of Science, 43, 290–292.CrossRefGoogle Scholar
  4. Humphreys, P., & Freedman, D. (1996). The grand leap. The British Journal for the Philosophy of Science, 47, 113–118.CrossRefGoogle Scholar
  5. Korb, K. B., Hope, L. R., Nicholson, A. E., & Axnick, K. (2004). Varieties of causal intervention. In Pacific Rim International Conference on AI’04, pp. 322–331.Google Scholar
  6. Korb, K. B., Twardy, C., Handfield, T., & Oppy, G. (2005). Causal reasoning with causal models. Technical Report Monash University.Google Scholar
  7. Pearl, J. (2000). Causality: Models, reasoning and inference. New York: Cambridge University Press.Google Scholar
  8. Spirtes, P., Glymour C., & Scheines R. (1993). Causation, Prediction and Search. Springer VerlagGoogle Scholar
  9. Spirtes, P., Glymour, C., & Scheines R. (2000). Causation, prediction and search (2nd Ed.), MIT PressGoogle Scholar
  10. Steel, D. (2004, August). Biological redundancy and the faithfulness condition. In Causality, uncertainty and ignorance: Third international summer school, Univ of Konstanz, GermanyGoogle Scholar
  11. Verma, T. S., & Pearl J. (1990). Equivalence and synthesis of causal models. In Proceedings of the sixth conference on uncertainty in AI, pp. 220–227. Morgan KaufmannGoogle Scholar
  12. Woodward, J. (2003). Making things happen. OxfordGoogle Scholar
  13. Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5,(3) 161–215MATHGoogle Scholar

Copyright information

© Springer Science+Business Media 2006

Authors and Affiliations

  1. 1.School of Computer Science & Software EngineeringMonash UniversityClaytonAustralia
  2. 2.Department of History & Philosophy of ScienceUniversity of MelbourneParkvilleAustralia

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