Correlation vs. Causation

Correlation vs. Causation

• Correlation does not equal causation.
• Just because two variables are correlated, it doesn't mean they are causally linked.

Association Claims

• Association claims are non-committal; they don't assert a causal connection.
• Example: "Children who go to better preschools tend to get into better colleges."
  • Variables: Quality of preschool and quality of college.
  • Correlation: Positive (as one goes up, the other tends to go up).

Causal Claims

• Causal claims assert that one variable causes the other to change.
• Example: "Better preschools lead to better colleges."
  • Implies that preschool quality causes better college outcomes.
• Causal claims are stronger and require more evidence than association claims.

Wording

• Association claims use language like "tend to" or "are associated with."
• Causal claims use language like "leads to" or "causes."

Association vs. Causal Truth

• If a causal claim is true, the association claim is also true.
• However, a true association claim does not guarantee the causal claim is true.
• Example: Ice cream sales and drowning are correlated (association claim is true), but ice cream does not cause drowning (causal claim is false).

Establishing Causation

• Causal claims require ruling out confounds (other explanations for the relationship).
• To establish causation, manipulate one variable and measure its effect on the other.
• This often involves conducting a true experiment.

Types of Claims and Variables

• Frequency claims: Involve one measured variable.
• Association claims: Involve two measured variables.
• Causal claims: Involve two variables, where one is manipulated, and the other is measured.