4'2: Causation

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4.2 Causation

• Think about this A researcher randomly selects
  20 children and finds the height of their
  parents. He performs a linear regression of
  mothers height vs. fathers height and finds a
  strong positive correlation. Does he conclude
  that one variable caused/explains the other?
• What are some possible factors that might help
  explain these results?

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A. Causation

• When two variables have a direct cause and effect
  link.
• Ex. Mothers Body Mass Index vs. Daughters Body
  Mass Index.
• Heredity is the causal link
• Note Even when direct causation is present, it
  is rarely a complete explanation of the
  association between two variables.

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• Ex. An experiment on the amount of saccharin in a
  rats diet vs. the number of tumors in a rats
  bladder has shown a causal relationship
• Note We cannot generalize a causal relationship
  to other settings than the one observed!
• Best way to show evidence of causation is
  from experiments that actually change one
  variable while keeping all others fixed

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B. Common Response

• Observed association between two variables x and
  y is explained by a lurking variable z.
• Def Lurking Variable A variable that has an
  important effect on the relationship among the
  variables in a study but is not included among
  the variables studied.
• Could be any number of things, often use common
  sense to come up with another explanation.

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• Common Response can create an association where
  one may not exist.
• Ex. x SAT score
• y Fresh. Yr. GPA
• There would appear to be a positive association,
  but what is a lurking variable that could be
  having an effect on this relationship?

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• Ex. x of children
• y income
• Possible lurking variables?
• Ex. x of years of education
• y of books in a personal library
• Possible lurking variables??

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C. Counfounding

• Two variables are confounded when their effects
  on a response variable cannot be distinguished
  from each other. The confounded variables may be
  either explanatory or lurking variables.
• What are some possible confounding variables in
  the mother vs. daughter BMI case?

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• Ex. What might a confounding variable be in each
  case?
• x whether a person regularly attends religious
  services
• y lifespan
• Confounding variable
• 2. x number of pieces of fruit eaten regularly
• y number of cavities
• Confounding variable

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D. Establishing Causation

• Conduct a carefully designed experiment in which
  the effects of possible lurking variables are
  controlled. (Chapter 5)
• Social/Political Issues often hard to establish
  causal relationship since an experiment is not
  possible from an ethical standpoint.

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• Ex. Power lines and Leukemia there is no way
  to conduct any experiment. But, through research
  and case studies there has only been a chance
  connection shown.
• Ex. Smoking vs. Lung cancer certain benchmarks
  have been met to infer causation.
• Association is strong
• Association is consistent in multiple countries
  and at different times
• Higher doses stronger response
• Cause precedes the effect in time
• Cause is plausible