Doubly Robust Proxy Causal Learning with Neural Mean Embeddings

• URL: http://arxiv.org/abs/2605.09514v1
• Date: Sun, 10 May 2026 12:50:25 GMT
• Title: Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
• Abstract summary: We develop a neural doubly robust framework for proxy causal learning with continuous and structured treatments.<n>The framework covers population, heterogeneous, and conditional dose-response functions, yielding full response-curve estimators.<n>Across synthetic and image-valued benchmarks, the proposed estimators outperform existing baselines and single-bridge neural estimators.
• Score: 58.8808794752741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning addresses this problem through bridge equations involving treatment- and outcome-inducing proxies, avoiding direct recovery of the latent confounder. Existing doubly robust proxy estimators combine outcome and treatment bridges, but typically rely on fixed kernels, sieves, or low-dimensional semiparametric models; existing neural proxy methods are more flexible, but are largely single-bridge estimators. We develop a neural doubly robust framework for proxy causal learning with continuous and structured treatments. Our method introduces a neural mean-embedding estimator for the treatment bridge, combines it with a neural outcome bridge, and estimates the doubly robust correction through a final regression stage. The framework covers population, heterogeneous, and conditional dose-response functions, yielding full response-curve estimators rather than binary-treatment effects. The algorithms use two stages for each bridge and history-aware updates of the final linear layers to stabilize stochastic multi-stage training. We prove consistency of the algorithms showing that the doubly robust error is controlled by the final averaging and regression errors together with the smaller of the outcome- and treatment-side weak-norm bridge errors. Across synthetic and image-valued benchmarks, the proposed estimators outperform existing baselines and single-bridge neural estimators, showing the benefit of combining learned outcome and treatment bridges in a doubly robust construction. Our implementation is available at https://github.com/BariscanBozkurt/DRPCL-Neural-Mean-Embedding.

Related papers

• Causal Inference with Categorical Unobserved Confounder via Mixture Learning [20.542714966688656]
  We show that causal effects are identifiable when the unobserved confounder is categorical under suitable conditions.<n>We propose an estimation procedure based on tensor decomposition, which allows consistent recovery of the latent structure.
  arXiv  Detail & Related papers  (2026-05-18T18:28:16Z)
• Cross-fitted Proximal Learning for Model-Based Reinforcement Learning [3.5690236380446163]
  In offline settings with hidden confounding, models learned directly from observational data may be biased.<n>Recent work has shown that policy evaluation in such confounded partially observable Markov decision processes (POMDPs) can be reduced to estimating reward-emission and observation-transition bridge functions satisfying conditional moment restrictions (CMRs)
  arXiv  Detail & Related papers  (2026-04-06T21:29:57Z)
• Robust low-rank estimation with multiple binary responses using pairwise AUC loss [0.0]
  Multiple binary responses arise in many modern data-analytic problems.<n>Low-rank models offer a natural way to encode latent dependence across tasks.<n>Existing methods for binary data are largely likelihood-based and focus on pointwise classification.
  arXiv  Detail & Related papers  (2026-01-13T15:00:10Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting.<n>Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines.<n> Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Estimation and Inference for Causal Functions with Multiway Clustered Data [6.988496457312806]
  This paper proposes methods of estimation and uniform inference for a general class of causal functions. The causal function is identified as a conditional expectation of an adjusted (Neyman-orthogonal) signal. We apply the proposed methods to analyze the causal relationship between levels in Africa and the historical slave trade.
  arXiv  Detail & Related papers  (2024-09-10T17:17:53Z)
• C-Learner: Constrained Learning for Causal Inference [4.370964009390564]
  We propose a novel debiasing approach that achieves the best weighting of both worlds, producing stable plug-in estimates.<n>Our constrained learning framework solves for the best plug-in estimator under the constraint that the first-order error with respect to the plugged-in quantity is zero.
  arXiv  Detail & Related papers  (2024-05-15T16:38:28Z)
• Flexible Nonparametric Inference for Causal Effects under the Front-Door Model [2.6900047294457683]
  We develop novel one-step and targeted minimum loss-based estimators for both the average treatment effect and the average treatment effect on the treated under front-door assumptions.<n>Our estimators are built on multiple parameterizations of the observed data distribution, including approaches that avoid mediator density entirely.<n>We show how these constraints can be leveraged to improve the efficiency of causal effect estimators.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction [51.82628081279621]
  Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions. A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems. It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution.
  arXiv  Detail & Related papers  (2023-09-02T01:27:53Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.