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Interpreting Probability in Causal Models for Cancer

Interpreting Probability in Causal Models for Cancer. Federica Russo & Jon Williamson Philosophy – University of Kent. Overview. Cancer epidemiology Interpretations of probability Desiderata Frequency- cum -Objective Bayesianism Risks, odds and probabilities. Cancer epidemiology.

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Interpreting Probability in Causal Models for Cancer

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  1. Interpreting Probability in Causal Models for Cancer Federica Russo & Jon Williamson Philosophy – University of Kent

  2. Overview • Cancer epidemiology • Interpretations of probability • Desiderata • Frequency-cum-Objective Bayesianism • Risks, odds and probabilities

  3. Cancer epidemiology • A double objective • Establishing generic claims Non-smokers have a statistically significant greater risk (25%) of lung cancer if their spouses are smokers • Applying the generic in the single-case Audry, who has metastatic breast cancer, will survive more than 5 years, to extent 0.4 • Both are probabilistic statements

  4. Interpretations on the market • Classical and logical • P = ratio # of favourable cases / # of all equipossible cases • Physical: frequency and propensity • P = limiting relative frequency of an attribute in a reference class • P = tendency of a type of physical situation to yield an outcome • Subjective • P = quantitative expression of an agent’s opinion, degree of belief or epistemic attitude • Objective Bayesian • P = degree of belief shaped on empirical and logical constraints

  5. Desiderata • Objectivity Account for the objectivity of probability • Calculi Explain how we reason about probability • Epistemology Explain how we can know about probability • Variety Cope with the full variety of probabilistic claims • Parsimony Be ontologically parsimonious

  6. Let’s bargain

  7. Deal! Frequency-cum-ObjectiveBaysianism • Pluralism is a viable option: • Generic causal claims require a frequency interpretation • Single-case causal claims require an objective Bayesian interpretation • Objective Bayesianism has pragmatic virtues

  8. Risks, Odds and Probabilities:Easy to compute Risks and odds compare proportions

  9. Risks, Odds and Probabilities:Tricky to interpret • … a RR equal to 2.0 means that an unexposed person is twice as likely to have and adverse outcome as one who is not exposed … (Sistrom & Garvan 2004) • … odds and probabilities are different ways of expressing the chance that an outcome may occur… (Sistrom & Garvan 2004) • … the probability that a child with eczema will also have fever is estimated by the proportion 141/561 (25.1%) … (Bland & Altman 2000)

  10. To sum up • In the context of cancer epidemiology: • Two categories of causal claims: Generic – single-case • These are probabilistic • The market offers: Classical/Logical, Physical, Subjective, Objective Bayesian • We went for: Frequency-cum-Objective Bayesianism

  11. Conclusions and … what next? Epidemiology: • looks for socio-economic & biological causes  Thus it’s paradigmatic of the social and health sciences • models causal relations with probabilities  Thus it raises genuine interest for the philosophy of causality and probability • is concerned with generic and single-case claims  Thus gives us further questions: the levels of causation

  12. Any comments, queries, objections, complaints about the paper?Please call the Helpdesk Many thanks to the British Academy and the FSR (UcLouvain) for funding the project:Causality and the Interpretation of Probability in the Social and Health Scienceswww.kent.ac.uk/secl/philosophy/jw/2006/CausalityProbability.htm

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