Statistics and Causality (and Bayes)

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”

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“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.

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