Conjoint experiments randomize multidimensional profiles, offering a powerful design for recovering structural preference parameters -- including marginal rates of substitution, willingness to pay, and the distribution of preferences across a population. Yet the dominant approach in political science has focused on nonparametric causal estimands that do not leverage this potential. We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. The neural network addresses the concern that a parametric specification may not capture the true data generating process, while double/debiased machine learning provides valid inference on average preference parameters. We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup.

翻译：联合实验通过随机化多维属性组合，为恢复结构性偏好参数（包括边际替代率、支付意愿及人群中偏好的分布）提供了强有力的设计。然而，政治学的主流方法一直聚焦于非参数因果估计量，未能充分利用这一潜力。我们提出了一种结构方法，将深度神经网络嵌入随机效用逻辑模型，使偏好参数能够作为受访者特征的完全灵活函数而变化。神经网络解决了参数模型可能无法捕捉真实数据生成过程的担忧，而双重/去偏机器学习则提供了对平均偏好参数的有效推断。我们将该方法应用于三项重要的联合研究，发现简化形式平均值掩盖了丰富的偏好异质性：近乎零的性别效应与83%的受访者偏好女性候选人并存，对不民主行为的反对近乎普遍但在强度上差异显著，累进税偏好跨越了每个党派子群体。