Synthese

, Volume 193, Issue 4, pp 1029–1046 | Cite as

Green and grue causal variables

S.I.: The Philosophy of Clark Glymour

Abstract

The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes (2007) showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no “correct” set of causal variables. I extend Spirtes’ result to the general framework of linear structural equation models and then explore to what extent the possibility to intervene or a preference for simpler causal systems may help in selecting among sets of causal variables.

Keywords

Causality Intervention Bayes nets Variable definition 

References

  1. Bollen, K. A. (1989). Structural equations with latent variables. Hoboken: Wiley.CrossRefGoogle Scholar
  2. Cartwright, N. (2001). What is wrong with bayes nets? The Monist, 242–264.Google Scholar
  3. Eberhardt, F. (2014). Direct causes and the trouble with soft interventions. Erkenntnis, 79, 755–777.CrossRefGoogle Scholar
  4. Geiger, D., Verma, T., & Pearl, J. (1990). Identifying independence in Bayesian networks. Networks, 20, 507–533.CrossRefGoogle Scholar
  5. Glymour, C. (2004). Review of James Woodward’s ‘Making things happen’. British Journal of Philosophy of Science, 55, 779–790.CrossRefGoogle Scholar
  6. Glymour, C. (2006). Markov properties and quantum experiments. In W. Demopoulos & I. Pitowsky (Eds.), Physical theory and its interpretation: Essays in honor of Jeffrey Bub. New York: Springer.Google Scholar
  7. Glymour, C. (2007). Statistical jokes and social effects: Intervention and invariance in causal relations. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 294–300). Oxford: Oxford University Press.CrossRefGoogle Scholar
  8. Glymour, C., & Glymour, M. R. (2014). Commentary: Race and sex are causes. Epidemiology, 25(4), 488–490.Google Scholar
  9. Goodman, N. (1955). Fact, fiction and forecast. Indianapolis: Bobbs-Merrill.Google Scholar
  10. Haavelmo, T. (1944). The probability approach in econometrics. Econometrica, 12(Supplement), iii–vi+1–115.Google Scholar
  11. Hernán, M. A., & VanderWeele, T. J. (2011). Compound treatments and transportability of causal inference. Epidemiology, 22(3), 368–377.CrossRefGoogle Scholar
  12. Hyttinen, A., Eberhardt, F., & Hoyer, P. O. (2012). Learning linear cyclic causal models with latent variables. Journal for Machine Learning Research, 13, 3387–3439.Google Scholar
  13. Hyttinen, A., Eberhardt, F., & Järvisalo, M. (2014). Constraint-based causal discovery: Conflict resolution with answer set programming. In Proceedings of the 30th conference on Uncertainty in Artificial Intelligence (pp. 340–349). Edinburgh: AUAI Press.Google Scholar
  14. Lacerda, G., Spirtes, P., Ramsey, J., & Hoyer, P.O. (2008). Discovering cyclic causal models by independent components analysis. In Proceedings of the 24th conference on Uncertainty in Artificial Intelligence (pp. 366–374).Google Scholar
  15. Pearl, J. (2000). Causality. Oxford: Oxford University Press.Google Scholar
  16. Ramsey, J., Zhang, J., & Spirtes, P. (2006). Adjacency-faithfulness and conservative causal inference. In Proceedings of the 22nd annual conference on Uncertainty in Artificial Intelligence. Arlington, VA: AUAI Press.Google Scholar
  17. Richardson, T. (1996). Feedback models: Interpretation and discovery. PhD thesis, Carnegie Mellon.Google Scholar
  18. Shalizi, C., & Moore, C. (2003). What is a macrostate: Subjective observations and objective dynamics.Google Scholar
  19. Shimizu, S., Hoyer, P. O., Hyvarinen, A., & Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7, 2003–2030.Google Scholar
  20. Sober, E. (2001). Venetian sea levels, British bread prices, and the principle of common cause. British Journal of Philosophy of Science, 52, 331–346.CrossRefGoogle Scholar
  21. Spirtes, P. (2007). Variable definition and causal inference. In Proceedings of the international Congress for Logic, Methodology and Philosophy of Science.Google Scholar
  22. Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. New York: Springer. (2nd ed. MIT Press 2000).CrossRefGoogle Scholar
  23. Spirtes, P., & Scheines, R. (2004). Causal inference of ambiguous manipulations. Philosophy of Science, 71(5), 833–845.CrossRefGoogle Scholar
  24. Spohn, W. (2000). Bayesian nets are all there is to causal dependence. In M. C. Galavotti, et al. (Eds.), Stochastic dependence and causality (pp. 157–172). Stanford: CSLI Publications.Google Scholar
  25. Strotz, R. H., & Wold, H. O. A. (1960). Recursive vs. nonrecursive systems: An attempt at synthesis (part i of a triptych on causal chain systems). Econometrica, 28(2), 417–427.CrossRefGoogle Scholar
  26. Woodward, J. (2003). Making things happen. Oxford: Oxford University Press.Google Scholar
  27. Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5, 161–215.CrossRefGoogle Scholar
  28. Zhang, J., & Spirtes, P. (2003). Strong faithfulness and uniform consistency in causal inference. In Proceedings of of the 19th conference on Uncertainty in Artificial Intelligence (pp. 632–639).Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.CaltechPasadenaUSA

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