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Does Association Imply Causation?

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.

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Does Association Imply Causation?

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  1. 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:

  2. 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)

  3. 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 relationship. • What is the lurking variable in these two examples? • 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.

  4. 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 variables. • But you often see them used interchangeably…

  5. Association, however strong, does NOT imply causation. Only careful experimentation can show causation - but see Examples 2.43 and 2.44 Association and causation Not all examples are so obvious…

  6. We can evaluate the association using the following criteria: The association is strong. The association is consistent. Higher doses are associated with stronger responses. Alleged cause precedes the effect. The alleged cause is plausible. 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 doesn’t CAUSE lung cancer? HW: read 2.6, go over all the examples in the section (esp. 2.43, 2.44) and then look at # 2.133-2.145

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