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Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
Interpreting Causality
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Interpreting Causality

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  • 1. Interpreting causality in the health sciences Federica Russo & Jon Williamson Philosophy, University of Kent
  • 2. Causality and probability in the sciences
    • An ongoing project at Kent
    • www.kent.ac.uk/secl/philosophy/jw/2006/CausalityProbability.htm
    • In the sciences
    • About probability
      • We defend an
      • ObjectiveBayesian-cum-Frequency interpretation
    • About causality
      • We defend an epistemic interpretation
  • 3. Overview
    • Two types of evidence
      • Probabilistic
      • Mechanistic
    • Against causal monism
    • Against causal pluralism
    • The case for epistemic causality
  • 4. Probabilistic evidence
    • Observed dependencies
    • in a range of similar studies
    • Coherent results
    • Tests for stability
    • in structural models
  • 5. Mechanistic evidence
    • Biomedical mechanisms
      • Chemical reactions, electric signals,
      • alterations at the cellular level, …
    • A plausible (physiological) link
    • from the cause to the effect
  • 6. We need both types of evidence
    • Semmelweis and puerperal fever
      • He had statistics but the link wasn’t accepted
      • till backed with the mechanism
    • Helicobacter pylory
      • The causal relation was hypothesised
      • based on probabilistic evidence and
      • accepted when backed with the mechanism
  • 7. A classic: Bradford Hill’s criteria
    • Strength of association
    • Temporality
    • Consistency
    • Theoretical plausibility
    • Coherence
    • Specificity in the causes
    • Dose response relationship
    • Experimental evidence
    • Analogy
  • 8. Contemporary medicine: IARC
    • IARC reviews published studies
    • Assessment of causality depends on:
      • Presence of a plausible mechanism
      • Probabilistic evidence
      • (e.g. frequencies, risks)
  • 9. Monistic accounts won’t do
    • Mechanistic accounts
      • Causal processes intersect with each other
      • in interactive forks
    • Probabilistic accounts
      • Causes make a difference
      • in the probability of the effect
      • (ceteris paribus)
    • Problem:
    • Neither can handle the dual aspect
    • of causal epistemology
  • 10. Pluralistic accounts won’t do … either
    • Uniformity of causal language:
    • A single notion of cause is used
    • The pluralist rebuts:
      • A mechanistic cause 1
      • A probabilistic cause 2
      • Different meanings of cause
      • But each refers to a single concept!
    • Therefore, the pluralist has
    • twice as the problems of the monist!
  • 11. The way out: epistemic causality
    • Rational causal beliefs:
      • The agent’s evidence determines
      • which beliefs to adopt
      • A causal relation is the set of causal beliefs
      • that an agent with total evidence
      • should adopt
  • 12. Constraints on causal beliefs
    • The agent’s causal beliefs should account
    • for all known dependencies
    • that are not already accounted for
    • by non-causal dependencies
    • The agent’s causal beliefs should be
    • compatible with other knowledge
    • The agent should not have causal beliefs
    • that are not warranted by her evidence
  • 13. An application: epistemic causality in cancer science
    • Dataset of clinical observations of past patients
    • Dataset of observations at molecular level
    • Probabilistic evidence
    • Knowledge of biological mechanisms
    • Mechanistic evidence
    • Knowledge of semantic relations between variables
    • Pro/contra mechanistic evidence
  • 14. To sum up
    • The health sciences need and employ two types of evidence
    • Monistic accounts won’t do
    • Pluralistic accounts won’t do either
    • The epistemic account succeeds
  • 15. To conclude … and to research ahead
    • There is a key distinction between
      • Evidence from which we draw causal conclusions
      • and the concept of cause
    • A single epistemic concept suits
    • the case of the health sciences
    • … and possibly the social and natural sciences too

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