Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
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, they impose restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic substitution patterns. We propose an amortized inference approach that relies on a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. We develop a specialized neural network architecture designed to respect the in...
| Authors | Easton Huch, Michael Keane |
|---|---|
| Primary category | stat.ME |
| Published | 25 March 2026 |
| Last updated at source | 7 September 2026 |
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