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Capits Presentation


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Capits Presentation

  1. 1. Interpreting Probability in Causal Models for Cancer Federica Russo & Jon Williamson Philosophy – University of Kent
  2. 2. Overview <ul><li>Cancer epidemiology </li></ul><ul><li>Interpretations of probability </li></ul><ul><li>Desiderata </li></ul><ul><li>Frequency- cum -Objective Bayesianism </li></ul><ul><li>Risks, odds and probabilities </li></ul>
  3. 3. Cancer epidemiology <ul><li>A double objective </li></ul><ul><ul><li>Establishing generic claims </li></ul></ul><ul><ul><li>Non-smokers have a statistically significant greater risk (25%) of lung cancer if their spouses are smokers </li></ul></ul><ul><ul><li>Applying the generic in the single-case </li></ul></ul><ul><ul><li>Audry, who has metastatic breast cancer, will survive more than 5 years, to extent 0.4 </li></ul></ul><ul><li>Both are probabilistic statements </li></ul>
  4. 4. Interpretations on the market <ul><li>Classical and logical </li></ul><ul><ul><li>P = ratio # of favourable cases / # of all equipossible cases </li></ul></ul><ul><li>Physical: frequency and propensity </li></ul><ul><ul><li>P = limiting relative frequency of an attribute in a reference class </li></ul></ul><ul><ul><li>P = tendency of a type of physical situation to yield an outcome </li></ul></ul><ul><li>Subjective </li></ul><ul><ul><li>P = quantitative expression of an agent’s opinion, </li></ul></ul><ul><ul><li>degree of belief or epistemic attitude </li></ul></ul><ul><li>Objective Bayesian </li></ul><ul><ul><li>P = degree of belief shaped on empirical and logical constraints </li></ul></ul>
  5. 5. Desiderata <ul><li>Objectivity </li></ul><ul><ul><li>Account for the objectivity of probability </li></ul></ul><ul><li>Calculi </li></ul><ul><ul><li>Explain how we reason about probability </li></ul></ul><ul><li>Epistemology </li></ul><ul><ul><li>Explain how we can know about probability </li></ul></ul><ul><li>Variety </li></ul><ul><ul><li>Cope with the full variety of probabilistic claims </li></ul></ul><ul><li>Parsimony </li></ul><ul><ul><li>Be ontologically parsimonious </li></ul></ul>
  6. 6. Let’s bargain Class/ Log Prop Freq Subj Emp- Based Obj Bayes Objectivity     Calculi   Epistemology      Variety    Parsimony  
  7. 7. Deal! Frequency- cum -ObjectiveBaysianism <ul><li>Pluralism is a viable option: </li></ul><ul><ul><li>Generic causal claims require </li></ul></ul><ul><ul><li>a frequency interpretation </li></ul></ul><ul><ul><li>Single-case causal claims require </li></ul></ul><ul><ul><li>an objective Bayesian interpretation </li></ul></ul><ul><li>Objective Bayesianism have </li></ul><ul><li>pragmatic virtues </li></ul>
  8. 8. Risks, Odds and Probabilities: Easy to compute <ul><li>Risks and odds compare proportions </li></ul>Factor Disease Yes No Exposed n 11 p 11 n 12 p 12 Unexposed n 21 p 21 n 22 p 22
  9. 9. Risks, Odds and Probabilities: Tricky to interpret <ul><li>… 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 … </li></ul><ul><ul><li>(Sistrom & Garvan 2004) </li></ul></ul><ul><li>… odds and probabilities are different ways of expressing the chance that an outcome may occur… </li></ul><ul><ul><li>(Sistrom & Garvan 2004) </li></ul></ul><ul><li>… the probability that a child with eczema will also have fever is estimated by the proportion 141/561 (25.1%) … </li></ul><ul><ul><li>(Bland & Altman 2000) </li></ul></ul>
  10. 10. To sum up <ul><li>In the context of cancer epidemiology: </li></ul><ul><ul><li>Two categories of causal claims: </li></ul></ul><ul><ul><li>Generic – single-case </li></ul></ul><ul><ul><li>These are probabilistic </li></ul></ul><ul><li>The market offers: </li></ul><ul><ul><li>Classical/Logical, Physical, </li></ul></ul><ul><ul><li>Subjective, Objective Bayesian </li></ul></ul><ul><li>We went for: </li></ul><ul><ul><li>Frequency- cum -Objective Bayesianism </li></ul></ul>
  11. 11. Conclusions and … what next? <ul><li>Epidemiology: </li></ul><ul><ul><li>looks for socio-economic & biological causes </li></ul></ul><ul><ul><ul><li> Thus it’s paradigmatic of the </li></ul></ul></ul><ul><ul><ul><li>social and health sciences </li></ul></ul></ul><ul><ul><li>models causal relations with probabilities </li></ul></ul><ul><ul><ul><li> Thus it raises genuine interest for the philosophy of causality and probability </li></ul></ul></ul><ul><ul><li>is concerned with generic and single-case claims </li></ul></ul><ul><ul><ul><li> Thus gives us further questions: </li></ul></ul></ul><ul><ul><ul><li>the levels of causation </li></ul></ul></ul>
  12. 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 Sciences