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Summary on causation
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A brief description of summary on causation
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Terms DefinitionsEpidemiology the branch of medical science that
investigates all the factors that determine
the presence or absence of diseases and
disorders
Causal Interpretation Problem do not tell us how to understand the causal
import of a measure of association on those
occasions when it is used to express a
causal fact, as well as a fact about an
association
Probalistic Approach C causes E iff C raises the probability of E
Problem When an association is measured, the claim
that is being made is that is just that the
exposure raises the probability of a disease.
‘Probability raising is what our measures of
association measure before they have been
causally interpreted.’ (p. 41) The CIP is
concerned with what more is needed to
explain the causative relation
Counterfactual Approach If C causes E, then if C were not to occur, E
would not occur. counterfactual statement
is a conditional statement (if... , then…) that
is contrary to the facts
Problem First, effects do not always counterfactually
depend on their causes.
Second, effects depend on more things
than we call “causes”
Explanatory A measure of strength of association is a
measure of causal strength iff the exposure
explains the measured net difference in
outcome.
Explanations There are different kinds of explanations –
total and contrastive. Total explanations
provide a complete and comprehensive
account of an occurrence. A contrastive
explanation tells us how much of a
difference a factor makes between two
things. What is the difference between lung
cancer in smokers and in non-smokers?
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