Probabilistic Causality
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In this important first book in the series Cambridge Studies in Probability, Induction and Decision Theory, Ellery Eells explores and refines current philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic ca...
In this important first book in the series Cambridge Studies in Probability, Induction and Decision Theory, Ellery Eells explores and refines current philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic causal interaction, disjunctive causal factors, and temporal ideas, Professor Eells advances the analysis of what it is for one factor to be a positive causal factor for another. A salient feature of the book is a new theory of token level probabilistic causation in which the evolution of the probability of a later event from an earlier event is central. This will be a book of crucial significance to philosophers of science and metaphysicians; it will also prove stimulating to many economists, psychologists, and physicists.
Table of contents:
Preface; Introduction; 1. Populations and probability; 2. Spurious correlation and probability increase; 3. Causal interaction and probability increase; 4. Causal intermediaries and transitivity; 5. Temporal priority, asymmetry, and some comparisons; 6. Token-level probabilistic causationl Appendixes; Bibliography; Index.
Table of contents:
Preface; Introduction; 1. Populations and probability; 2. Spurious correlation and probability increase; 3. Causal interaction and probability increase; 4. Causal intermediaries and transitivity; 5. Temporal priority, asymmetry, and some comparisons; 6. Token-level probabilistic causationl Appendixes; Bibliography; Index.
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