Mitigating Included- and Omitted-Variable Bias in Estimates of Disparate Impact

Understand

Managers, employers, policymakers, and others often seek to understand whether decisions are biased against certain groups.

• One popular analytic strategy is to estimate disparities after adjusting for observed covariates, typically with a regression model.
• This approach, however, suffers from two key statistical challenges.
• First, omitted-variable bias can skew results if the model does not adjust for all relevant factors; second, and conversely, included-variable bias -- a lesser-known phenomenon -- can skew results if the set of covariates includes irrelevant factors.