Abstract: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.
| Subjects: | Methodology (stat.ME); Econometrics (econ.EM) |
| Cite as: | arXiv:2604.10845 [stat.ME] |
| (or arXiv:2604.10845v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2604.10845 arXiv-issued DOI via DataCite |
From: Yiqing Xu [view email]
[v1]
Sun, 12 Apr 2026 22:35:04 UTC (298 KB)
[v2]
Mon, 25 May 2026 17:00:30 UTC (298 KB)
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