Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
For researchers estimating individual-level causal effects from combined experimental and observational data, this work provides a practical solution to systematic biases in finite-sample PNS estimation.
The paper proposes a neural framework for estimating Probability of Necessity and Sufficiency (PNS) bounds on individual treatment effects, addressing finite-sample issues like constraint violations and extremum bias. The method achieves nominal coverage and exact constraint validity in high-dimensional settings where standard estimators fail.
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.