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stat.ME2026
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Xinyan Su, Jiacan Gao, Mingyuan Ma +6
We study uplift estimation for combinatorial treatments. Uplift measures the pure incremental causal effect of an intervention (e.g., sending a coupon or a marketing message) on us…
stat.ME2026
Individualized Causal Effects under Network Interference with Combinatorial Treatments
Yunping Lu, Haoang Chi, Qirui Hu +1
Modern causal decision-making increasingly demands individualized treatment-effect estimation in networks where interventions are high-dimensional, combinatorial vectors. While net…
stat.ME2026
Group Permutation Testing in Linear Model: Sharp Validity, Power Improvement, and Extension Beyond Exchangeability
Zonghan Li, Hongyi Zhou, Zhiheng Zhang
We consider finite-sample inference for a single regression coefficient in the fixed-design linear model , where may exhibit…