AI RESEARCH
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
arXiv CS.LG
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ArXi:2509.24962v2 Announce Type: replace The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-called meta-learners) often perform poorly in the presence of low overlap. In this work, we