[2604.00915] Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects

Computer Science > Machine Learning arXiv:2604.00915 (cs) [Submitted on 1 Apr 2026] Title:Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects Authors:Haorui Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel View a PDF of the paper titled Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects, by Haorui Ma and 3 other authors View PDF Abstract: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 introduce the LT-O-learners (Long-Term Orthogonal Learners), a set of novel orthogonal learners for HLTE estimation. The learners are designed for the canonical HLTE setting that combines a short-term randomized dataset $\mathcal{D}_1$ with a long-term historical dataset $\mathcal{D}_2$. The key idea of our LT-O-Learners is to retarget the learning objective by introducing custom overlap weights that downweight samples with low overlap in treatment or in long-term observation. We show that the retargeted loss is equivalent to the weighted oracle loss and satisfies Neyman-orthogonality, which means our...