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endogeneity_bias
Endogeneity bias arises when an independent variable in a regression model is correlated with the error term, violating a core assumption of ordinary least squares estimation. This can occur through omitted variables, measurement error, or simultaneous causation. The result is biased and inconsistent coefficient estimates that do not reflect true causal relationships.
A study examines whether police presence reduces crime by regressing crime rates on the number of officers. However, cities with more crime hire more police, so police presence is endogenous — it is both a potential cause and a consequence of the crime rate.
A marketing analyst regresses a brand's sales on its advertising spend and finds a weak positive effect, concluding advertising barely works. In reality, the company increases advertising precisely when sales are already declining, making ad spend negatively correlated with underlying demand — the reverse causality attenuates the true effect.
Researchers studying whether higher wages reduce employee absenteeism find almost no relationship in their regression. However, firms that already experience high absenteeism tend to raise wages to retain staff, creating reverse causality that obscures the genuine negative effect of wages on absenteeism.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Is there reason to suspect the independent variable is correlated with the error term in the model?
Type: binaryCould there be simultaneous causation between the independent and dependent variables?
Type: binaryIs the analysis treating an endogenous variable as if it were exogenous?
Type: binaryHas the study failed to use instrumental variables or other techniques to address endogeneity?
Type: binaryEndogeneity bias arises when an independent variable in a regression model is correlated with the error term, violating a core assumption of ordinary least squares estimation. This can occur through omitted variables, measurement error, or simultaneous causation. The result is biased and inconsistent coefficient estimates that do not reflect true causal relationships.
Many real-world relationships involve feedback loops or shared unobserved causes. Standard regression assumes the independent variable is determined outside the system, but this assumption often fails in observational data.
Use instrumental variable estimation, natural experiments, or regression discontinuity designs. Clearly articulate why a variable is believed to be exogenous. Test for endogeneity using Hausman tests or similar diagnostics.
Pervasive in economics and policy evaluation, such as estimating the effect of education on earnings (ability confounds both), or the impact of institutions on economic growth.
Excluding a relevant confounding variable from a model biases the estimated effects.
The presumed effect is actually the cause, reversing the true causal direction.
Failing to account for a third variable that influences both the independent and dependent variables, creating a spurious apparent relationship. The 'lurking variable' problem that undermines causal claims from observational data.
Gathering data on multiple variables but omitting non-significant ones from report.
Use these tools to detect, analyze, or train this aspect.