Correlation vs Causation
Correlation measures the strength and direction of association between two variables; causation implies that changes in one variable directly produce changes in another. A strong correlation (e.g., r = 0.9) does not prove causation. Classic examples abound: shoe size and reading ability are correlated in children (confounded by age), but shoe size does not cause reading ability. Understanding when correlation implies causation requires evaluating study design, confounding variables, temporal precedence, and mechanism. Randomized experiments offer the strongest causal evidence; observational studies must carefully control for confounders.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. · ISBN 978-0-521-89560-6
- Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. · DOI 10.1037/h0037350
- Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300. · DOI 10.1177/003591576505800503
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