Density Ratio-Free Doubly Robust Proxy Causal Learning
Not on the map. Why?
Abstract (arXiv)
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 benchmarks, including a prior doubly robust method that requires both kernel smoothing and density ratio estimation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 26 May 2025.
Builds on
24 of 57 references.
References · 24
- 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
- 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
- 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 133
- 2017Sobolev Norm Learning Rates for Regularized Least-Squares Algorithmcited by 114
- 2020A Measure-Theoretic Approach to Kernel Conditional Mean Embeddingscited by 86
- 2020Semiparametric proximal causal inferencecited by 59
- 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 55
- 2022Optimal Rates for Regularized Conditional Mean Embedding Learningcited by 50
- 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 44
- 2021Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluationcited by 40
- 2015The Influence Function of Semiparametric Estimatorscited by 36
- 2020Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curvescited by 36
12 more
- 2019A Rigorous Theory of Conditional Mean Embeddingscited by 31
- 2020Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instrumentscited by 28
- 2013Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variablescited by 24
- 2023Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithmcited by 22
- 2018Visually Communicating and Teaching Intuition for Influence Functionscited by 19
- 2020Kernel Conditional Moment Test via Maximum Moment Restrictioncited by 18
- 2022Deep Learning Methods for Proximal Inference via Maximum Moment Restrictioncited by 14
- 2024Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimalcited by 8
- 2023Kernel Single Proxy Control for Deterministic Confoundingcited by 5
- 2018Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learningcited by 4
- 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
- 2025Density Ratio-based Proxy Causal Learning Without Density Ratioscited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.