Neural Doubly Robust Proximal Causal Estimation

Ruolin Meng, Dhanajit Brahma, Ricardo Henao, Lawrence Carin
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:3331-3339, 2026.

Abstract

We consider the challenging task of estimating treatment effects from observational data under the assumption that there are unobserved confounders. We employ the proximal causal estimation framework, that assumes access to control (proxy) measurements that contain information about unobserved confounders. We consider outcome and treatment bridges, which provide two distinct ways of estimating causal effects. We also consider a doubly-robust approach, based on combining the outcome and treatment bridges, which is robust in expectation to either (but not both) of the two bridge functions being misspecified. We present a new theoretical bound on the estimation accuracy of the treatment bridge, and we analyze the variance of the doubly-robust estimator. We investigate the impact of autoencoder-based regularization through an ablation study, finding that simpler models sometimes outperform more complex variants. Comparisons with state-of-the-art methods on synthetic and real-world data demonstrate the advantages of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-meng26b, title = { Neural Doubly Robust Proximal Causal Estimation }, author = {Meng, Ruolin and Brahma, Dhanajit and Henao, Ricardo and Carin, Lawrence}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {3331--3339}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/meng26b/meng26b.pdf}, url = {https://proceedings.mlr.press/v300/meng26b.html}, abstract = { We consider the challenging task of estimating treatment effects from observational data under the assumption that there are unobserved confounders. We employ the proximal causal estimation framework, that assumes access to control (proxy) measurements that contain information about unobserved confounders. We consider outcome and treatment bridges, which provide two distinct ways of estimating causal effects. We also consider a doubly-robust approach, based on combining the outcome and treatment bridges, which is robust in expectation to either (but not both) of the two bridge functions being misspecified. We present a new theoretical bound on the estimation accuracy of the treatment bridge, and we analyze the variance of the doubly-robust estimator. We investigate the impact of autoencoder-based regularization through an ablation study, finding that simpler models sometimes outperform more complex variants. Comparisons with state-of-the-art methods on synthetic and real-world data demonstrate the advantages of our approach. } }
Endnote
%0 Conference Paper %T Neural Doubly Robust Proximal Causal Estimation %A Ruolin Meng %A Dhanajit Brahma %A Ricardo Henao %A Lawrence Carin %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-meng26b %I PMLR %P 3331--3339 %U https://proceedings.mlr.press/v300/meng26b.html %V 300 %X We consider the challenging task of estimating treatment effects from observational data under the assumption that there are unobserved confounders. We employ the proximal causal estimation framework, that assumes access to control (proxy) measurements that contain information about unobserved confounders. We consider outcome and treatment bridges, which provide two distinct ways of estimating causal effects. We also consider a doubly-robust approach, based on combining the outcome and treatment bridges, which is robust in expectation to either (but not both) of the two bridge functions being misspecified. We present a new theoretical bound on the estimation accuracy of the treatment bridge, and we analyze the variance of the doubly-robust estimator. We investigate the impact of autoencoder-based regularization through an ablation study, finding that simpler models sometimes outperform more complex variants. Comparisons with state-of-the-art methods on synthetic and real-world data demonstrate the advantages of our approach.
APA
Meng, R., Brahma, D., Henao, R. & Carin, L.. (2026). Neural Doubly Robust Proximal Causal Estimation . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:3331-3339 Available from https://proceedings.mlr.press/v300/meng26b.html.

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