An interesting paper seeks to improve accuracy by approaching discrimination detection and discrimination remedies with distinct empirical strategies. In Improved Statistical Methods for the Calculation of Damages in Discrimination Lawsuits, Scott Susin (HUD) and Ioan Voicu (OCC) note that: "While regression techniques for detecting discrimination have generated a vast literature, similar techniques for remedying discrimination have been comparatively neglected." The authors propose Bayes techniques for compensation calculations. The paper's abstract follows.
"In this paper, we develop a new method that uses empirical Bayes techniques to more accurately incorporate the individual-specific information. While some non-injurious factors will always be unobserved, it is possible to estimate the percentage of the variance due to these factors. Given this estimate, our method calculates individual-specific damages by using empirical Bayes methods that combine a general estimate, based on a regression coefficient, with individual-specific estimates based on regression residuals. It thus combines many of the best features of previous approaches and results in considerably more accurate payments than existing statistical procedures."