[2505.19807] Density Ratio-Free Doubly Robust Proxy Causal Learning

Computer Science > Machine Learning arXiv:2505.19807 (cs) [Submitted on 26 May 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Density Ratio-Free Doubly Robust Proxy Causal Learning Authors:Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier, Liyuan Xu, Arthur Gretton View a PDF of the paper titled Density Ratio-Free Doubly Robust Proxy Causal Learning, by Bariscan Bozkurt and 4 other authors View PDF HTML (experimental) Abstract:We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. Two main approaches have been proposed: outcome bridge-based and treatment bridge-based methods. In this work, we propose two kernel-based doubly robust estimators that combine the strengths of both approaches, and naturally handle continuous and high-dimensional variables. Our identification strategy builds on a recent density ratio-free method for treatment bridge-based PCL; furthermore, in contrast to previous approaches, it does not require indicator functions or kernel smoothing over the treatment variable. These properties make it especially well-suited for continuous or high-dimensional treatments. By using kernel mean embeddings, we propose the first density-ratio free doubly robust estimators for proxy causal learning, which have closed form solutions and strong uniform consistency guarantees. Our estimators outperform existing methods on PCL benchmar...