5 papers · 1 filter
Wasserstein Policy Learning for Distributional Outcomes
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates…
Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
Yunfeng Wang, Zhiheng Zhang, Zijun Gao
Partial identification provides informative causal guarantees when point identification is impossible, but existing approaches based on optimal transport (OT) become computationall…
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…
Feasible Fusion: Constrained Joint Estimation under Structural Non-Overlap
Yuxi Du, Zhiheng Zhang, Haoxuan Li +4
Causal inference in modern largescale systems faces growing challenges, including highdimensional covariates, multi-valued treatments, massive observational (OBS) data, and limited…
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…