PARTIAL IDENTIFICATION OF COUNTERFACTUAL CHOICE PROBABILITIES*
展示当行为模型只能部分识别选择概率时，如何利用可观测的选择概率推断总体中类型的分布，进而预测未实现选择情境下的行为。
This article shows how to predict counterfactual discrete choice behavior when the presumed behavioral model partially identifies choice probabilities. The simple, general approach uses observable choice probabilities to partially infer the distribution of types in the population and then applies the results to predict behavior in unrealized choice settings. Two illustrative applications are given. One assumes only that persons have strict preferences. The other assumes strict preferences and utility functions that are linear in attribute bundles, with no restrictions on the shape of the distribution of preference parameters.