Editing Causal Inference (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Correlation vs. Causation
! Feature !! Correlation !! Causation
|-
| Symmetry || Symmetric (If A correlates with B, B correlates with A) || Asymmetric (A causes B, but B doesn't necessarily cause A)
|-
| Prediction || Good for "What usually happens?" || Good for "What happens if I change things?"
|-
| Math || Covariance, Pearson's r || Do-calculus, DAGs, Structural Equations
|-
| Requirement || Observation || Intervention or clever identification
|}

'''Collider Bias''': This is a tricky trap. If you control for a variable that is caused by ''both'' your treatment and your outcome, you can accidentally create a fake correlation where none existed. For example, if you only study "Famous Actors," you might find that "Acting Talent" and "Physical Beauty" are negatively correlated—not because they are in real life, but because you need ''one'' of them to be famous in the first place.
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