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4'3 Establishing Causation

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CAUSATION- when there is an association ( or -) between two variables, and it ... Even well-established casual relations may not generalize into other settings! 8 ... – PowerPoint PPT presentation

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Title: 4'3 Establishing Causation


1
4.3 Establishing Causation
2
Causation
  • We would like to think that the change in the
    explanatory variable causes a change in the
    response variable.

3
3 ways to explain association
  • 1st way to explain association
  • CAUSATION- when there is an association ( or -)
    between two variables, and it is direct
    cause-and-effect relationship.
  • Ex calories consumed/weight

4
The dashed line shows the association. The solid
line shows the direct link between x and y.
5
Example of Causation
  • A study showed that there is a strong correlation
    (r.505) between mothers BMI and their
    daughters BMI.
  • Other variables were controlled like hrs. of TV,
    physical activity, etc.

6
Example of Causation
  • In this BMI experiment, the mothers BMI only
    explained 25.6 of the variability among the
    daughters BMI.
  • Even when direct causation is present, it is
    rarely a complete explanation of an association
    between 2 variables!

7
Example of Causation
  • If we hold all other factors fixed, and just
    change the x-variable, if the y-variable changes,
    then we have good reason to think x caused a
    change in y!

8
Example of Causation
  • A study gave rats different amount of saccharin
    and larger amounts caused tumors in the rats
    bladders.
  • Though we dont experiment on humans, a study
    of human consumption of different amounts of
    saccharin showed little association between
    saccharin and tumors
  • Even well-established casual relations may not
    generalize into other settings!

9
3 ways of explaining association
  • 2nd way Common Response
  • Common response is when a lurking variable is
    present and the association between x and y is
    explained by some lurking variable, z.

10
Changes in both x and y are caused by changes in
a lurking variable, z. They are both influenced
by z so an association is present.
  • There actually may be NO DIRECT LINK between x
    and y, but z makes it appear that there is!
  • If there is an association between x and y,
    lurking variable z makes it appear stronger!

11
Example of Common Response
No direct link between SAT scores and grades in
college
  • X students SAT scores
  • Y high grades in college
  • Z students ability and knowledge (lurking
    variable)

12
Example of Common Response
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