AuthorOTTE, RICHARD EDWARD.
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PublisherThe University of Arizona.
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AbstractProbability and Causality is a critical analysis of the problem of causality in indeterministic contexts. Most philosophers who have written about probabilistic causality feel that Hume's requirement of constant conjunction should be replaced by a requirement of positive statistical relevance. After arguing that a theory of probabilistic causality is necessary to account for many causal relations, Hume's theory of probabilistic causality is analyzed. Although Hume's theory is inadequate, it does form the basis for later discussions of probabilistic causality. The first modern treatment of probabilistic causality is that of Hans Reichenbach, and it is discussed in detail since all later theories rely upon his basic intuitions. Reichenbach presented a proof that probabilistic definitions of causality were equivalent to the non-probabilistic analyses based on mark transmission. This proof is analyzed, and although it fails, several possible modifications of it are discussed. The next theory discussed is that of Patrick Suppes. It is shown that Suppes' theory is intrinsically defective, and that no minor modifications of his theory will be sufficient to solve the problems it faces. I. J. Good's quantitative theory of causation is then also shown to be defective. Although almost all theories of probabilistic causality assume that causes raise the probability of their effects, there is no real defense of that requirement. The author attempts to clear up the confusion surrounding the discussions of this requirement by showing that two related but distinct causal concepts are being confused. The related problem of Simpson's paradox is then discussed, and it is shown that all proposed solutions to it face serious philosophical problems. Salmon developed a theory of probabilistic causality which analyzes causal relations in terms of mark transmission instead of probability relations. The counterfactual aspect of mark transmission and causal interaction is closely examined. Probability and Causality concludes with an appendix in which the various interpretations of probability are discussed in reference to developing a theory of probabilistic causality.