UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation
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Computer Science > Machine Learning
Title:UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation
Abstract:Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on which estimator performs best; we show the disagreement is substantially about metrics, not models. UpliftBench evaluates 12 uplift estimators under an outer-test-isolated, multi-objective protocol across seven dataset families; its two findings are identified where a reference objective exists -- F1 on the standard continuous benchmark (IHDP), F2 in a within-sample case study on Jobs. On that benchmark, Qini shows no detectable alignment with effect accuracy -- across all 100 IHDP realizations its mean rank correlation with effect accuracy is +0.07, 95% CI [-0.03, +0.16] -- while AUUC is consistently more aligned (paired prefix-mean-AUUC-over-Qini gap +0.49 [+0.40, +0.59]; the shipped cumulative-gain AUUC aligns better still, +0.73). On Jobs, ranking metrics are structurally insufficient for a sign-threshold policy because they discard the score level; empirically, within the released split-rotation analysis direct policy-risk selection yields lower benchmark regret than random model selection while Qini, AUUC, and uplift-at-$k$ do not (14-15% regret). Calibrating the decision threshold removes 81% of the Qini-selection regret. Both findings are bounded, not universal: F1 is not detected on either validation family (the ACIC and Revenue-Synthetic gaps are both indistinguishable from zero), and F2 vanishes under a budgeted-value objective where rank suffices. UpliftBench releases versioned loaders, fixed protocols, result artifacts, and a reproducible living leaderboard; the public repository accompanies the paper.
| Comments: | 25 pages, 9 figures. Code, data, and results: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.00915 [cs.LG] |
| (or arXiv:2608.00915v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.00915
arXiv-issued DOI via DataCite (pending registration)
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