Causation, Prediction, and Accommodation

This page was last edited on 12/14/99 by Malcolm R Forster

Publication Data

Forster, Malcolm R. (in preparation): "Causation, Prediction, and Accommodation." (Draft July 1997)

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Abstract

Causal inference is commonly viewed in two steps: (1) Represent the empirical data in terms of a probability distribution. (2) Draw causal conclusions from the conditional independences exhibited in that distribution. I challenge this reconstruction by arguing that the empirical data are often better partitioned into subsets and represented by a separate probability distributions within each domain. For then their similarities and the differences provide a wealth of relevant causal information. Computer simulations confirm this hunch, and the results are explained in terms of a distinction between prediction and accommodation, and William Whewell’s consilience of inductions. If the diagnosis is correct, then the standard notion of the empirical distinguishability, or equivalence, of causal models needs revision, and the idea that cause can be defined in terms of probability is far more plausible than before.

  

 

 

 

Main References

Humphreys, Paul and David Freedman (1997): "The Grand Leap." British Journal for the Philosophy of Science 47: 113-123.

Spirtes, Peter, Clark Glymour and Richard Scheines (1993): Causation, Prediction and Search. New York: Springer-Verlag.

Verma, T. S. and Judea Pearl (1991): "Equivalence and Synthesis of Causal Models," in P. P. Bonissone, M. Henrion, L.N. Kanaland J.F. Lemmer (eds.) Uncertainty in Artificial Intelligence 6. Amsterdam, Elslevier Science Publishers, pp. 255-268.