fairGATE - Fair Gated Algorithm for Targeted Equity
Fairness-aware correction of a prediction model for binary
and continuous outcomes with two or more protected groups and
their intersections. A cross-fitted base learner
(gradient-boosted trees or a neural network) is fitted without
the protected attribute, and a gated mixture of neural experts
learns a per-person correction to its score under a fairness
penalty (a random-effects or variance penalty on
outcome-stratified group means, or a thresholded worst-group
penalty on error rates), with a cross-fitted disparity signal,
an intervention cost, smoothed early stopping, a per-person
export of adjustments with bootstrap stability intervals, and
routing analyses of which expert corrects whom. Methods draw on
mixture-of-experts neural networks by Jordan and Jacobs (1994)
<doi:10.1007/978-1-4471-2097-1_113>, gradient boosting by Chen
and Guestrin (2016) <doi:10.1145/2939672.2939785>,
cross-fitting by Chernozhukov et al. (2018)
<doi:10.1111/ectj.12097>, equalised odds by Hardt, Price and
Srebro (2016) <doi:10.48550/arXiv.1610.02413>, the
random-effects variance estimator of DerSimonian and Laird
(1986) <doi:10.1016/0197-2456(86)90046-2>, and personalised
treatment prediction for depression by Iniesta, Stahl and
McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.
