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Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental samples, making it difficult to estimate heterogeneous effects from high-dimensional covariates. In such settings, policymakers and medical practitioners often succumb to the curse of dimensionality or apply off-the-shelf dimension reduction methods that may not preserve treatment heterogeneity. Yet these domains often come with large historical datasets measuring a wide range of outcomes -- a source of supervision that is rarely exploited in pra

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