arXiv Machine Learning September 3, 2026 By Qianqian Wang, Yunshan Li, Jiawen Zeng, Wenwu Gong, Lili Yang

GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation

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GenCAR introduces a method for out‑of‑distribution recommendation that balances utility and risk by controlling the proxy‑label false discovery rate (FDR). It frames the problem as an α‑Valid Counterfactual Recommendation (α‑VCR) task, coupling counterfactual supervision with calibrated set selection using conformal p‑values and Benjamini–Hochberg filtering. The approach theoretically bounds counterfactual approximation error and guarantees finite‑sample, distribution‑free FDR control under various dependence assumptions, and empirical results show improved OOD candidate recovery across benchmarks.

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