Package: fairGATE 1.0.0

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>.

Authors:Rhys Holland [aut, cre], Raquel Iniesta [aut]