Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks

• URL: http://arxiv.org/abs/2605.07065v1
• Date: Fri, 08 May 2026 00:17:15 GMT
• Title: Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
• Abstract summary: In finite samples, standard plug-in estimators systematically fail.<n>We propose a neural framework for finite-sample PNS estimation that resolves both pathologies.
• Score: 3.52150916506216
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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