Deciding that A causes B when in fact B causes A is a mistake called omitted variable bias.

Name: Deciding that A causes B when in fact B causes A is a mistake called omitted variable bias.
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This mistake is called reverse causality, not omitted variable bias. Omitted variable bias occurs when a model fails to include one or more relevant variables, leading to incorrect conclusions about relationships between other variables.

Deciding That a Causes B When in Fact B Causes