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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

Interpreting Probability in Causal Models for Cancer

Federica Russo & Jon Williamson

Philosophy – University of Kent

  • Cancer epidemiology
  • Interpretations of probability
  • Desiderata
  • Frequency-cum-Objective Bayesianism
  • Risks, odds and probabilities
cancer epidemiology
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
interpretations on the market
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
  • 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

deal frequency cum objectivebaysianism
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

risks odds and probabilities easy to compute
Risks, Odds and Probabilities:Easy to compute

Risks and odds compare proportions

risks odds and probabilities tricky to interpret
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)

to sum up
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

conclusions and what next
Conclusions and … what next?


  • 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

any comments queries objections complaints about the paper please call the helpdesk

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