Process / pipelinecausal-reasoning
相关性与因果性
相关性衡量两个变量之间关联的强度和方向；因果性则意味着一个变量的变化直接导致另一个变量的变化。强相关性（例如，r = 0.9）并不证明因果关系。经典例子比比皆是：儿童的鞋码与阅读能力相关（受年龄混淆），但鞋码不会导致阅读能力。要理解何时相关性意味着因果性，需要评估研究设计、混淆变量、时间先后顺序和作用机制。随机实验提供了最强的因果证据；观察性研究必须仔细控制混淆因素。
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来源
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Understanding the Distinction Between Correlation and Causation in Research. ScholarGate. https://scholargate.app/zh/research-statistics/correlation-vs-causation
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