AI RESEARCH
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
arXiv CS.LG
•
ArXi:2603.24705v1 Announce Type: cross 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, cons