Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914542876884992 |
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| author | Patil, Gandharv Tang, Keyi Aoki, Raquel Guelman, Leo |
| author_facet | Patil, Gandharv Tang, Keyi Aoki, Raquel Guelman, Leo |
| contents | Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level causality through intersection bounds derived from combined experimental and observational data. In finite samples, however, standard plug-in estimators systematically fail: they violate structural probability constraints and suffer from extremum bias induced by max-min operators, yielding spuriously narrow intervals. We propose a neural framework for finite-sample PNS estimation that resolves both pathologies. We introduce an anchored neural architecture that guarantees structural constraint satisfaction by construction. To correct extremum bias, we employ precision-corrected intersection-bound inference, leveraging Epistemic Neural Networks for scalable, high-dimensional uncertainty quantification. Empirical evaluations confirm that this approach maintains nominal coverage and exact constraint validity in high-dimensional regimes where standard estimators systematically undercover. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07065 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks Patil, Gandharv Tang, Keyi Aoki, Raquel Guelman, Leo Machine Learning Artificial Intelligence Econometrics Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level causality through intersection bounds derived from combined experimental and observational data. In finite samples, however, standard plug-in estimators systematically fail: they violate structural probability constraints and suffer from extremum bias induced by max-min operators, yielding spuriously narrow intervals. We propose a neural framework for finite-sample PNS estimation that resolves both pathologies. We introduce an anchored neural architecture that guarantees structural constraint satisfaction by construction. To correct extremum bias, we employ precision-corrected intersection-bound inference, leveraging Epistemic Neural Networks for scalable, high-dimensional uncertainty quantification. Empirical evaluations confirm that this approach maintains nominal coverage and exact constraint validity in high-dimensional regimes where standard estimators systematically undercover. |
| title | Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks |
| topic | Machine Learning Artificial Intelligence Econometrics |
| url | https://arxiv.org/abs/2605.07065 |