An Omitted Variables Approach to Disentangling Differential Prediction in College Admissions

Abstract Standardized tests used in college admissions have raised fairness concerns, with critics arguing bias against underrepresented groups. Recent research suggests that apparent differential prediction may reflect omitted variables (OVs), which are unmeasured factors that systematically differ across groups and influence performance. We examined 499,622 Black and White SAT takers across 142 institutions to test whether OVs help account for prediction differences in forecasting first‐year GPA using SAT scores, high school GPA, and both. Black students’ college grades were overpredicted by .11–.24 GPA points, while SES showed smaller effects. Adding omitted variables reduced Black overprediction by 54%–72%. High school SES and racial composition showed the largest effects, suggesting that similar admission scores signal different preparation levels depending on school context. Our findings highlight that typically unmeasured differences that shape academic performance, rather than flaws in traditional predictors, account for a substantial portion of differential prediction.