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Correlation-Causation Fallacy

Also Known As: Cum hoc ergo propter hoc Illusory causation
Aspect ID: correlation_causation_fallacy

Definition

The correlation-causation fallacy is the error of inferring a causal relationship between two variables solely because they are statistically correlated. A correlation can arise from direct causation, reverse causation, confounding, or chance. It is distinct from the reverse causality fallacy and from spurious correlation, though all three involve misinterpreting correlational evidence.

Examples

Cities with more hospitals have higher death rates. Does building hospitals cause death? No — more hospitals are built in cities with more sick people. The causal arrow runs from disease burden to hospital construction, while death follows from disease, not from hospitals.

Data show that children who have more books in their home score higher on literacy tests. A school district launches a program to distribute free books to low-income households, expecting test scores to rise automatically. The correlation likely reflects parental education and engagement — simply adding books without addressing those underlying factors produces little effect.

A study finds that people who carry lighters are significantly more likely to develop lung cancer. A naive reading suggests lighters cause cancer. In reality, carrying a lighter is a proxy for smoking behavior — the lighter is correlated with cancer only because it is associated with the true causal agent.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Does the argument infer a causal relationship from an observed correlation without additional evidence?

    Type: binary
  2. 2

    Have plausible confounders been controlled for in the analysis?

    Type: binary
  3. 3

    Is reverse causality (the effect causing the exposure) being ruled out?

    Type: binary
  4. 4

    Is a plausible biological, social, or mechanical mechanism identified and tested?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.