Quick questions on Correlation versus causation: HSC Investigating Science Module 7

A statistical association between two variables. As variable X changes, variable Y tends to change in a related way.

A directed relationship where changes in X produce changes in Y. Three minimum conditions:

Three common reasons a correlation can exist without causation.

In 1965 the epidemiologist Sir Austin Bradford Hill proposed nine criteria for establishing causation from observational evidence. The criteria are not a checklist but a set of considerations to weigh.

In the 1950s, observational studies (Doll and Hill in the UK, Wynder and Graham in the US) found that smokers had much higher lung cancer rates than non-smokers. Tobacco companies and some scientists argued correlation was not causation.

For many important questions (smoking, climate, diet over decades), randomised trials are unethical or impractical. Causal inference relies on:

The website _Spurious Correlations_ lists hundreds of statistically significant but absurd correlations:

A third variable causes both. Ice cream sales and drowning deaths are correlated because both are caused by hot weather. Eating ice cream does not cause drowning.

Y causes X, not X causes Y. People with depression sometimes use cannabis to self-medicate. A correlation between cannabis use and depression might reflect depression leading to cannabis use, rather than cannabis causing depression.

Random fluctuations produce statistical associations in large datasets. With 100 random tests, on average 5 will appear "significant" at p < 0.05 by chance alone.

Markers reward at least three or four, applied to a specific case.

Some important questions cannot ethically be answered by RCTs. Observational studies with Bradford Hill criteria can be enough.

Without confounder analysis, observational claims are weak. :::