[2603.24705] Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks

Statistics > Methodology arXiv:2603.24705 (stat) [Submitted on 25 Mar 2026] Title:Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks Authors:Easton Huch, Michael Keane View a PDF of the paper titled Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks, by Easton Huch and Michael Keane View PDF HTML (experimental) Abstract:Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference approach using a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. Our proposal includes a specialized neural network architecture and accompanying training procedures designed to respect the invariance properties of discrete choice models. We provide group-theoretic foundations for the architecture, including a proof of universal approximation given a minimal set of invariant features. Once trained, the emulator enables rapid likelihood evaluation and gradient computat...