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

TL;DR

This paper introduces a neural framework for accurately estimating individual treatment effects using intersection bounds, ensuring structural constraints and reliable uncertainty quantification in high-dimensional data.

Contribution

It presents an anchored neural architecture combined with precision-corrected inference leveraging Epistemic Neural Networks to address bias and constraint violations in finite-sample PNS estimation.

Findings

Maintains nominal coverage in high-dimensional regimes.

Guarantees structural constraint satisfaction by design.

Reduces extremum bias in finite-sample estimates.

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.