AI RESEARCH
Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
arXiv CS.LG
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ArXi:2604.00915v1 Announce Type: new Estimation of heterogeneous long-term treatment effects (HLTEs) is widely used for personalized decision-making in marketing, economics, and medicine, where short-term randomized experiments are often combined with long-term observational data. However, HLTE estimation is challenging due to limited overlap in treatment or in observing long-term outcomes for certain subpopulations, which can lead to unstable HLTE estimates with large finite-sample variance. To address this challenge, we