Bayesianism and Causality, or, Why I am Only a Half-Bayesian

Part of the Applied Logic Series book series (APLS, volume 24)


I turned Bayesian in 1971, as soon as I began reading Savage’s monograph The Foundations of Statistical Inference [Savage, 1962]. The arguments were unassailable: (i) It is plain silly to ignore what we know, (ii) It is natural and useful to cast what we know in the language of probabilities, and (iii) If our subjective probabilities are erroneous, their impact will get washed out in due time, as the number of observations increases.


Structural Equation Model Directed Acyclic Graph Causal Model Causal Analysis Statistical Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [Balke and Pearl, 1994]
    A. Balke and J. Pearl. Probabilistic evaluation of counterfactual queries. In Proceedings of the Twelfth National Conference on Artificial Intelligence, volume I, pages 230–237. MIT Press, Menlo Park, CA, 1994.Google Scholar
  2. [Balke and Pearl, 1995]
    A. Balke and J. Pearl. Counterfactuals and policy analysis in structural models. In P. Besnard and S. Hanks, editors, Uncertainty in Artificial Intelligence 11, pages 11–18. Morgan Kaufmann, San Francisco, 1995.Google Scholar
  3. [Cartwright, 1989]
    N. Cartwright. Nature’s Capacities and Their Measurement. Clarendon Press, Oxford, 1989.Google Scholar
  4. [Cox, 1958]
    D.R. Cox. The Planning of Experiments. John Wiley and Sons, NY, 1958.Google Scholar
  5. [Dawid, 2000]
    A.P. Dawid. Causal inference without counterfactuals (with comments and rejoinder). Journal of the American Statistical Association, 95 (450): 407–448, June 2000.CrossRefGoogle Scholar
  6. [DeFinetti, 1974]
    B. DeFinetti. Theory of Probability: A Critical Introductory Treatment, 2 volumes (Translated by A. Machi and A. Smith ). Wiley, London, 1974.Google Scholar
  7. [Duncan, 1975]
    O.D. Duncan. Introduction to Structural Equation Models. Academic Press, New York, 1975.Google Scholar
  8. [Eells, 1991]
    E. Eells. Probabilistic Causality. Cambridge University Press, Cambridge, MA, 1991.CrossRefGoogle Scholar
  9. [Fine, 1985]
    K. Fine. Reasoning with Arbitrary Objects. B. Blackwell, New York, 1985.Google Scholar
  10. [Fisher, 1970]
    F.M. Fisher. A correspondence principle for simultaneous equations models. Econometrica, 38 (1): 73–92, January 1970.CrossRefGoogle Scholar
  11. [Galles and Pearl, 1997]
    D. Galles and J. Pearl. Axioms of causal relevance. Artificial Intelligence, 97 (1–2): 9–43, 1997.CrossRefGoogle Scholar
  12. [Galles and Pearl, 1998]
    D. Galles and J. Pearl. An axiomatic characterization of causal counterfactuals. Foundation of Science, 3 (1): 151–182, 1998.CrossRefGoogle Scholar
  13. [Goldberger, 1972]
    A.S. Goldberger. Structural equation models in the social sciences. Econometrica: Journal of the Econometric Society, 40: 979–1001, 1972.CrossRefGoogle Scholar
  14. Hall, 1998] N. Hall. Two concepts of causation, 1998. In press.Google Scholar
  15. Halpern, 19981 J.Y. Halpern. Axiomatizing causal reasoning. In G.F. Cooper and S. Moral, editors, Uncertainty in Artificial Intelligence, pages 202–210. Morgan Kaufmann, San Francisco, CA, 1998.Google Scholar
  16. [Heckman, 2001]
    J.J. Heckman. Econometrics and empirical economics. Journal of Econometrics, 100 (I): 1–5, 2001.CrossRefGoogle Scholar
  17. [Koopmans, 1953]
    T.C. Koopmans. Identification problems in econometric model construction. In W.C. Hood and T.C. Koopmans, editors, Studies in Econometric Method, pages 27–48. Wiley, New York, 1953.Google Scholar
  18. [Lewis, 1986]
    D. Lewis. Philosophical Papers. Oxford University Press, New York, 1986.Google Scholar
  19. [Lindley and Novick, 1981]
    D.V. Lindley and M.R. Novick. The role of exchangeability in inference. The Annals of Statistics, 9 (1): 45–58, 1981.CrossRefGoogle Scholar
  20. [Marschak, 1950]
    J. Marschak. Statistical inference in economics. In T. Koopmans, editor, Statistical Inference in Dynamic Economic Models, pages 1–50. Wiley, New York, 1950. Cowles Commission for Research in Economics, Monograph 10.Google Scholar
  21. Neyman, 1923] J. Neyman. On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science,5(4):465–480, 1990. [Translation]Google Scholar
  22. [Otte, 1981]
    R. Otte. A critique of Suppes’ theory of probabilistic causality. Synthese, 48: 167–189, 1981.CrossRefGoogle Scholar
  23. [Pearl, 1994]
    J. Pearl. A probabilistic calculus of actions. In R. Lopez de Mantaras and D. Poole, editors, Uncertainty in Artificial Intelligence 10, pages 454–462. Morgan Kaufmann, San Mateo, CA, 1994.Google Scholar
  24. [Pearl, 1995a]
    J. Pearl. Causal diagrams for empirical research. Biometrika, 82 (4): 669–710, December 1995.CrossRefGoogle Scholar
  25. [Pearl, 1995b]
    J. Pearl. Causal inference from indirect experiments. Artificial Intelligence in Medicine, 7 (6): 561–582, 1995.CrossRefGoogle Scholar
  26. [Pearl, 2000a]
    J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, 2000.Google Scholar
  27. [Pearl, 2000b]
    J. Pearl. Comment on A.P. Dawid’s, Causal inference without counterfactuals. Journal of the American Statistical Association, 95 (450): 428–431, June 2000.Google Scholar
  28. [Robins, 1987]
    J.M. Robins. A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. Journal of Chronic Diseases, 40 (Suppl 2): 139S - 161S, 1987.CrossRefGoogle Scholar
  29. Rubin, 19741 D.B. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66: 688–701, 1974.CrossRefGoogle Scholar
  30. [Savage, 1962]
    L. J. Savage. The Foundations of Statistical Inference. Methuen and Co. Ltd., London, 1962.Google Scholar
  31. [Simon and Rescher, 1966]
    H.A. Simon and N. Rescher. Cause and counterfactual. Philosophy and Science, 33: 323–340, 1966.CrossRefGoogle Scholar
  32. [Simon, 19531 H.A. Simon]
    Causal ordering and identifiability. In Wm. C. Hood and T.C. Koopmans,editors, Studies in Econometric Method, pages 49–74. Wiley and Sons, Inc., 1953.Google Scholar
  33. [Sobel, 19901 M.E. Sobel]
    Sobel, 19901 M.E. Sobel. Effect analysis and causation in linear structural equation models. Psychometrika, 55 (3): 495–515, 1990.Google Scholar
  34. [Spirtes et al,1993]
    P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search Springer-Verlag, New York, 1993.Google Scholar
  35. [Strotz and Wold, 1960]
    R.H. Strotz and H.O.A. Wold. Recursive versus nonrecursive systems: An attempt at synthesis. Econometrica, 28: 417–427, 1960.CrossRefGoogle Scholar
  36. [Suppes, 1970]
    P. Suppes. A Probabilistic Theory of Causality. North-Holland Publishing Co., Amsterdam, 1970.Google Scholar
  37. [Wright, 1921]
    S. Wright. Correlation and causation. Journal of Agricultural Research, 20: 557–585, 1921.Google Scholar

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© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of CaliforniaUSA

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