Probabilistic Causation Without Probability

Part of the Synthese Library book series (SYLI, volume 234)


The failure of Hume’s ‘constant conjunction’ to describe apparently causal relations in science and everyday life has led to various ‘probabilistic’ theories of causation of which (Suppes 1970) is an important example. A formal model that was developed for the analysis of comparative agricultural experiments in the first quarter of this century can be used to give an alternative account of ‘probabilistic causality’ that does not take a stand as to the stochastic or deterministic nature of the causal connection. The approach has many applications in social, behavioral and medical science. This paper discusses the formal model in detail, applies it to ‘probabilistic causation’ and compares the resulting theory to Suppes’s theory.


Causal Effect Causal Inference Granger Causality Prima Facie Causal Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Beecher, H. K.: 1966, ‘Pain: One Mystery Solved’, Science, 151, 840–841.CrossRefGoogle Scholar
  2. Cox, D. R.: 1958, The Planning of Experiments, John Wiley, New York.Google Scholar
  3. Efron, B. and Feldman. D.: 1991, ‘Compliance as an Explanatory Variable in Clinical Trials’, Journal of the American Statistical Association, 86, 9–26.CrossRefGoogle Scholar
  4. Fisher, R. A.: 1925, Statistical Methods for Research Workers, 1st ed., Oliver and Boyd, Edinburgh.Google Scholar
  5. Fisher, R. A.: 1926, ‘The Arrangement of Field Experiments’, Journal of Ministry of Agriculture of Great Britain, 33, 503–513.Google Scholar
  6. Florens, J. P. and Mouchart, M.: 1985, ‘A Linear Theory for Noncausality’, Econometrica, 53, 157–175.CrossRefGoogle Scholar
  7. Glymour, C.: 1986, ‘Statistics and Metaphysics’, Journal of the American Statistical Association, 81, 964–966.Google Scholar
  8. Good, I. J.: 1961, ‘A Causal Calculus’, British Journal of Science, 11, 305–318; 12, 43-51; (1962), 13, 88.CrossRefGoogle Scholar
  9. Granger, C. W. J.: 1969, ‘Investigating Causal Relations by Econometric Models and Cross-Spectral Methods’, Econometrica, 37, 424–438.CrossRefGoogle Scholar
  10. Granger, C. W. J.: 1986, ‘Comment’, Journal of the American Statistical Association, 81, 967–968.Google Scholar
  11. Holland, P. W.: 1986, ‘Statistics and Causal Inference’, Journal of the American Statistical Association, 81, 94–960.Google Scholar
  12. Holland, P. W.: 1988a, ‘Causal Inference, Path Analysis, and Recursive Structural Equations Models’, in C. C. Clogg (Ed.), Sociological Methodology, 1988, American Sociological Association, Washington, DC, pp. 449–484.Google Scholar
  13. Holland, P. W.: 1988b, ‘Causal Mechanism or Causal Effect: Which is Best for Statistical Science?’, Statistical Science, 3, 186–188.CrossRefGoogle Scholar
  14. Holland, P. W. and Rubin, D. B.: 1983, ‘On Lord’s Paradox’, in H. Wainer and S. Messick (Eds.), Principals [sic] of Modern Psychological Measurement, Lawrence Erlbaum, Hillsdale, NJ, pp. 3–25.Google Scholar
  15. Holland, P. W. and Rubin, D. B.: 1988, ‘Causal Inference in Retrospective Studies’, Evaluation Review, 12, 203–231.CrossRefGoogle Scholar
  16. Kadane, J. B. and Seidenfeld, T.: 1990, ‘Randomization in a Bayesian Perspective’, Journal of Statistical Planning and Inference, 25, 329–345.CrossRefGoogle Scholar
  17. Kempthorne, O.: 1952, The Design and Analysis of Experiments, John Wiley, New York.Google Scholar
  18. Lewis, D.: 1973, ‘Causation’, Journal of Philosophy, 70, 556–567.CrossRefGoogle Scholar
  19. Lund, T.: 1991, ‘Two Metamodels of Causal Effects’, Scandinavian Journal of Psychology, 32, 300–314.CrossRefGoogle Scholar
  20. Neyman, J.: 1935, ‘Statistical Problems in Agricultural Experimentation’, Supplement of the Journal of the Royal Statistical Society, 2, 107–180.CrossRefGoogle Scholar
  21. Neyman, J.: 1990, ‘On the Application of Probability Theory to Agricultural Experiments. Essay on Principles’, Translated and edited by D. M. Dabrowska and T. P. Speed, Statistical Science, 8, 465–472.Google Scholar
  22. Otte, R.: 1981, ‘A Critique of Suppes’ Theory of Probabilistic Causality’, Synthese, 48, 167–189.CrossRefGoogle Scholar
  23. Pratt, J. W. and Schlaifer, R.: 1984, ‘On the Nature and Discovery of Structure’, Journal of the American Statistical Association, 79, 9–21.CrossRefGoogle Scholar
  24. Pratt, J. W. and Schlaifer, R.: 1988, ‘On the Interpretation and Observation of Laws’, Journal of Econometrics, 39, 23–52.CrossRefGoogle Scholar
  25. Robins, J. M.: 1985, ‘A New Theory of Causality in Observational Survival Studies — Application of the Healthy Worker Effect’, Biometrics, 41, 311.Google Scholar
  26. Robins, J. M.: 1986 ‘A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period — Application to the Control of the Healthy Worker Survivor Effect’, Mathematical Modelling, 7, 1393–1512.CrossRefGoogle Scholar
  27. Robins, J. M.: 1987, ‘A Graphical Approach to the Identification and Estimation of Causal Parameters in Mortality Studies with Sustained Exposure Periods’, Journal of Chronic Diseases (Supplement 2), 40, 139S–161S.CrossRefGoogle Scholar
  28. Rosenbaum, P. R.: 1984a, ‘From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment’, Journal of the American Statistical Association, 79, 41–48.CrossRefGoogle Scholar
  29. Rosenbaum, P. R.: 1984b, ‘The Consequences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment’, Journal of the Royal Statistical Society, Series A, 147, 656–666.CrossRefGoogle Scholar
  30. Rosenbaum, P. R.: 1987, ‘The Role of a Second Control Group in an Observational Study’, Statistical Science, 2, 292–316.CrossRefGoogle Scholar
  31. Rosenbaum, P. R and Rubin, D. B.: 1984, ‘Estimating the effects caused by treatments’, Journal of the American Statistical Association, 79, 26–28.Google Scholar
  32. Rubin, D. B.: 1974, ‘Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies’, Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  33. Rubin, D. B.: 1977, ‘Assignment of Treatment Group on the Basis of a Covariate’, Journal of Educational Statistics, 2, 1–26.CrossRefGoogle Scholar
  34. Rubin, D. B.: 1978, ‘Bayesian Inference for Causal Effects: The Role of Randomization’, Annals of Statistics, 6, 34–58.CrossRefGoogle Scholar
  35. Rubin, D. B.: 1980, ‘Discussion’, Journal of the American Statistical Association, 75, 591–593.Google Scholar
  36. Rubin, D. B.: 1986, ‘Which Ifs have Causal Answers?’, Journal of the American Statistical Association, 81, 961–962.Google Scholar
  37. Rubin, D. B.: 1990a, ‘Formal Modes of Statistical Inference for Causal Effects’, Journal of Statistical Planning and Inference, 25, 279–292.CrossRefGoogle Scholar
  38. Rubin, D. B.: 1990b, ‘Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies’, Statistical Science, 5, 465–480.Google Scholar
  39. Salmon, W. C: 1989, Four Decades of Scientific Explanation, University of Minnesota Press, Minneapolis, MN.Google Scholar
  40. Skyrms, B.: 1988, ‘Probability and Causation’, Journal of Econometrics, 39, 53–68.CrossRefGoogle Scholar
  41. Sobel, M. E.: 1990, ‘Effect Analysis and Causation in Linear Structural Equation Models’, Psychometrika, 55, 495–515.CrossRefGoogle Scholar
  42. Sobel, M. E.: 1992, ‘Causal Inference in the Social and Behavioral Sciences’, in G. Arminger, C. C. Clogg, and M. E. Sobel (Eds.) A Handbookfor Statistical Modelling in the Social and Behavioral Sciences, Plenum Press, New York, pp. 1–38.Google Scholar
  43. Suppes, P. C: 1970, A Probabilistic Theory of Causality, North-Holland, Amsterdam.Google Scholar
  44. Tiles, J. E.: 1992, ‘Experimental Evidence vs. Experimental Practice’, British Journal of Philosophy, 43, 99–109.CrossRefGoogle Scholar


  1. Suppes, P.: 1970, A Probabilistic Theory of Causality, Amsterdam, North Holland.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  1. 1.Graduate School of Education, EPUniversity of CaliforniaBerkeleyUSA

Personalised recommendations