Causal Analysis: Causes, Results & Correlations.

Causal Analysis.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect.

Typically it involves establishing three elements: corelation, sequence in time / that is, causes must occur before their proposed effect /, and a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, often involving one or more experiments.


Causation, functional relation.

When we have:
  a Cause Ca₁,
  a Result R₁,
  a Proof that C₁ causes R₁

Then we can say that we have a Cause - Result relation between Ca₁ and R₁


Correlation, probabilistic relation.

'Correlation is not causation' means that just because two things correlate does not necessarily mean that one causes the other.

As a seasonal example, just because people in the UK tend to spend more in the shops when it's cold and less when it's hot doesn't mean cold weather causes frenzied high-street spending.

A more plausible explanation would be that cold weather tends to coincide with Christmas and the new year sales.


See also, if You wish: Causal Notation.