Synthese

, Volume 193, Issue 4, pp 1157–1176 | Cite as

Conditioning, intervening, and decision

Article

Abstract

Clark Glymour, together with his students Peter Spirtes and Richard Scheines, did pioneering work on graphical causal models (e.g. Spirtes et al., in Causation, prediction, and search, 2000). One of the central advances provided by these models is the ability to simply represent the effects of interventions. In an elegant paper (Meek and Glymour, in Br J Philos Sci 45: 1001–1021, 1994), Glymour and his student Christopher Meek applied these methods to problems in decision theory. One of the morals they drew was that causal decision theory should be understood in terms of interventions. I revisit their proposal, and extend the analysis by showing how graphical causal models might be used to address decision problems that arise in “exotic” situations, such as those involving crystal balls or time travelers.

Keywords

Causal decision theory Causal models Decision theory Glymour, Clark Meek, Christopher 

References

  1. Dummett, M. (1964). Bringing about the past. Philosophical Review, 73, 338–359.CrossRefGoogle Scholar
  2. Eberhardt, F., & Scheines, R. (2007). Interventions and causal inference. Philosophy of Science, 74, 981–995.CrossRefGoogle Scholar
  3. Eells, E. (1982). Rational decision and causality. Cambridge: Cambridge University Press.Google Scholar
  4. Egan, A. (2007). Some counterexamples to causal decision theory. Philosophical Review, 116, 93–114.CrossRefGoogle Scholar
  5. Gaifman, H. (1983). Paradoxes of infinity and self-applications I. Erkenntnis, 20, 131–155.CrossRefGoogle Scholar
  6. Gibbard, A., & Harper, W. (1978). Counterfactuals and two kinds of expected utility. In C. Hooker, J. Leach, & E. McClennen (Eds.), Foundations and applications of decision theory (pp. 125–162). Dordrecht: Reidel.Google Scholar
  7. Jeffrey, R. (1983). The logic of decision (2nd ed.). Chicago: University of Chicago Press.Google Scholar
  8. Joyce, J. (1999). The foundations of causal decision theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  9. Joyce, J. (2012). Ratifiability and stability in causal decision theory. Synthese, 187, 123–145.CrossRefGoogle Scholar
  10. Levi, I. (1985). Common causes, smoking, and lung cancer. In R. Campbell & L. Snowden (Eds.), Paradoxes of rationality and cooperation. Vancouver: UBC Press.Google Scholar
  11. Lewis, D. (1979). Counterfactual dependence and time’s arrow. Noûs 13, 455–476. (Reprinted in Lewis. (1986), pp. 32–52).Google Scholar
  12. Lewis, D. (1980). A subjectivist’s guide to objective chance. In R. Jeffrey (Ed.), Studies in inductive logic and probability (Vol. II, pp. 263–294). Berkeley: University of California Press. (Reprinted in Lewis. (1986), pp. 83–114).Google Scholar
  13. Lewis, D. (1981). Causal decision theory. Australasian Journal of Philosophy 59, 5–30. (Reprinted in Lewis. (1986), pp. 305–337).Google Scholar
  14. Lewis, D. (1986). Philosophical papers (Vol. II). Oxford: Oxford University Press.Google Scholar
  15. Meek, C., & Glymour, C. (1994). Conditioning and intervening. British Journal for the Philosophy of Science, 45, 1001–1021.CrossRefGoogle Scholar
  16. Nozick, R. (1969). Newcomb’s problem and two principles of rational choice. In N. Rescher (Ed.), Essays in honor of Carl G. Hempel (pp. 114–146). Dordrecht: Reidel.Google Scholar
  17. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  18. Price, H. (1986). Against causal decision theory. Synthese, 67, 195–212.CrossRefGoogle Scholar
  19. Price, H. (2012). Causation, chance, and the rational significance of supernatural evidence. Philosophical Review, 121, 483–538.CrossRefGoogle Scholar
  20. Richardson, T., & Robins, J. (2013). Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality. Technical Report, Center for Statistics and the Social Sciences, University of Washington, http://www.csss.washington.edu/Papers/wp128.pdf. Accessed 27 Feb 2015.
  21. Skyrms, B. (1980). Causal necessity. New Haven: Yale University Press.Google Scholar
  22. Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  23. Stern, R. (2014). Decision and intervention, unpublished manuscript.Google Scholar
  24. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Division of Humanities and Social Sciences, 101–40California Institute of TechnologyPasadenaUSA

Personalised recommendations