Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
通过等变神经网络实现相关误差离散选择模型的摊销推断
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…