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... present, there are still other variables possibly involved in the relationship. ... Alleged cause precedes the effect. The alleged cause is plausible. ... – PowerPoint PPT presentation

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Title: Does%20Association%20Imply%20Causation?

Does Association Imply Causation?
  • Sometimes, but not always! Look at example 2.42
    on page 149 (section 2.6, Explaining Causation)
    for several x,y variables where association was
    found - some are causal, others are not.
  • The figure 2.29 gives three possible scenarios
    explaining a found association between a response
    variable y and an explanatory variable x

  • Association between x and y can certainly be
    because changes in x cause y to change - but
    even when causation is present, there are still
    other variables possibly involved in the
    relationship. (See 1 in Ex. 2.42)
  • Be careful of applying a causal relationship
    between x and y in one setting to a different
    setting (2 shows a causal relationship in rats
    - does it extend to humans?)
  • Common response is an example of how a "lurking
    variable" can influence both x and y, creating
    the association between them (See 3)
  • Confounding between two variables arises when
    their effects on the response cannot be
    distinguished from each other - the confounding
    variables can either be explanatory or lurking
    (See 5)

Lurking variables
  • A lurking variable is a variable not included in
    the study design that does have an effect on the
    variables studied.
  • Lurking variables can falsely suggest a
  • What is the lurking variable in these two
  • Strong positive association between number of
    firefighters at a fire site and the amount of
    damage a fire does.
  • Negative association between moderate amounts
    of wine drinking and death rates from heart
    disease in developed nations.

Vocabulary lurking vs. confounding
  • A lurking variable is a variable that is not
    among the explanatory or response variables in a
    study and yet may influence the interpretation of
    relationships among those variables.
  • Two variables are confounded when their effects
    on a response variable cannot be distinguished
    from each other. The confounded variables may be
    either explanatory variables or lurking
  • But you often see them used interchangeably

Association and causation
  • Association, however strong, does NOT imply
  • Only careful experimentation can show causation -
    but see Examples 2.43 and 2.44

Not all examples are so obvious
Establishing causation It appears that lung
cancer is associated with smoking. How do we
know that both of these variables are not being
affected by an unobserved third (lurking)
variable? For instance, what if there is a
genetic predisposition that causes people to both
get lung cancer and become addicted to smoking,
but the smoking itself doesnt CAUSE lung cancer?
HW read 2.6, go over all the examples in the
section (esp. 2.43, 2.44) and then look at