False Cause

False-cause reasoning infers a causal relationship from association or sequence without adequate support for the causal step. The pattern may be real while its explanation remains uncertain. Evaluate relevant alternative explanations against the specific question.

A causal claim asks what would happen under different conditions. Improvement after a change supplies a timeline, not the missing comparison. Other changes, selection, or the ordinary course of events may explain the difference. Define the proposed cause precisely enough to identify a useful comparison.

Alternative explanations are hypotheses to investigate, not a checklist that automatically defeats causation. Randomization addresses some selection problems but still requires sound design and measurement. Observational evidence can inform causal judgment under explicit assumptions. Separate an observed association from an estimated effect and state the uncertainty relevant to action.

When to use it

When evaluating any causal claim, especially in business ('best practices'), health ('this supplement works'), or policy ('this program caused improvement'); when correlation is being presented as proof of causation; when designing experiments to establish genuine causation.

How it can help

Ask what would happen under a meaningful alternative condition and what evidence approximates that comparison. Investigate plausible competing explanations rather than treating association as sufficient proof or automatically worthless.