Full Description
Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself.
Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology.
This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required.
Contents
Introduction Part 1: Causality and Action 1. An action-related theory of causality 2. General discussion of AIM theories of causality 3. An example from medicine. Koch's work on bacterial diseases and his postulates Part 2: Causality and Mechanisms 4. Mechanistic theories of causality and causal theories of Mechanism 5. Types of evidence: (i) evidence of mechanism 6. Types of evidence: (ii) statistical evidence in human populations 7. Combining statistical evidence with evidence of mechanism 8. The Russo-Williamson thesis: (i) effects of smoking on health 9. The Russo-Williamson thesis: (ii) the evaluation of streptomycin and thalidomide 10. Objections to the Russo-Williamson thesis 11. Discovering cures in medicine and seeking for deeper explanations Part 3: Causality and Probability 12. Indeterministic causality 13. Causal networks 14. How should probabilities be interpreted? 15. Pearl's alternative approach to linking causality and probability 16. Extension of the action-related theory to the indeterministic case. Appendix 1. Example of a simple medical intervention which is not an intervention in Woodward's sense Appendix 2. Mathematical Terminology Appendix 3. Sudbury's Theorems Glossary of Medical Terms Index
