Statistics and Causality
From “Bayesianism and Causality, or, Why I am only a Half-Bayesian.”
- “correlation does not imply causation”
- “one cannot substantiate causal claims from associations alone, even at the population level—behind every causal conclusion there must lie some causal assumption that is not testable in observational studies.”
- “no causes in, no causes out”
- “we cannot convert statistical knowledge into causal knowledge”
“Examples of statistical concepts are: correlation, regression, dependence, conditional independence, association, likelihood, collapsibility, risk ratio, odd ratio, and so on.
Examples of causal concepts are: randomization, influence, effect, confounding, disturbance, spurious correlation, instrumental variables, intervention, explanation, attribution, and so on.”
A few more useful links on causality are here and here. Also on causal Bayesian networks here and causal learnings here.
