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stat.ME2026
Causal Influence Maximization with Steady-State Guarantees
Renjie Cao, Zhuoxin Yan, Zichen Wang +2
Classical influence maximization optimizes expected reach, which can be misaligned with welfare when exposures have heterogeneous, saturating, or adverse effects. We study seed sel…
stat.ME2026
Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
Jiacan Gao, Xinyan Su, Mingyuan Ma +7
Estimating heterogeneous treatment effects is central to data-driven decision-making, yet industrial applications often face a fundamental tension between limited randomized contro…
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…