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MENTAL MODEL · M3094

Omitted Variable Bias

Omitted Variable Bias
BusinessHigh supportEconomics
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Version 1.0.0 · Updated 2026-07-31

CORE DEFINITION

When a model omits a variable that affects both the dependent variable and is correlated with the independent variable, it leads to bias in the estimated effect of the independent variable. For example, finding that "ice cream sales" and "drowning incidents" are highly positively correlated is not only because they are related, but because the key variable "temperature" is omitted.

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Deep causal mining. In data analysis and review, it forces us to look for the "absent" factor. Do not easily trust superficial correlations; always ask: "What other hidden factors simultaneously affect both of these things?"

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In regression, if a confounding variable that affects both the independent and dependent variables is omitted, the coefficient estimates will be biased. The direction of the bias depends on the correlations between the omitted variable and both variables.

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Source support: Explicit

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/Omitted-variable_biasZH · Explicit
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