Third Cause Fallacy

The error of assuming causation between two correlated variables when both are actually caused by a hidden third variable. Ice cream sales and drowning deaths both increase in summer—not because ice cream causes drowning but because both are caused by hot weather (the third cause). In business: a new CEO and improved stock price may both be caused by improving market conditions. Meditation practice and stress reduction may both be caused by having free time. The third cause fallacy is the most common reason that 'correlation is not causation'—the hidden variable creates the correlation without either variable causing the other.

When to use it

When correlation is being interpreted as causation; when business 'best practices' are identified through correlation without controlling for confounders; when evaluating research claims that rely on observational data; when any two things seem to go together and a causal story is being constructed.

How it can help

For any observed correlation: actively search for third causes before accepting the causal story. The practice: when A and B are correlated, generate at least three plausible C variables that could cause both A and B independently. If the correlation disappears when you control for C, the correlation was spurious. In business: 'companies that do X have better performance'—is X causing performance, or is a third factor (like company size, industry, or management quality) causing both X and good performance?