Learning Preferences from Conjoint Data: A Structural Deep Learning Approach
Avidit Acharya, Jens Hainmueller, Yiqing Xu
TLDR
This paper introduces a structural deep learning method for conjoint data to uncover rich preference heterogeneity often missed by traditional approaches.
Key contributions
- Proposes a structural deep learning approach for conjoint data analysis.
- Embeds a deep neural network within a random utility logit model for flexible preference parameters.
- Employs double/debiased machine learning for robust inference on average preferences.
- Uncovers significant preference heterogeneity in real-world political science studies.
Why it matters
This paper addresses the limitations of traditional nonparametric causal estimands in conjoint analysis by leveraging the full potential of structural preference recovery. It provides a more nuanced understanding of preference heterogeneity, revealing insights previously masked by reduced-form averages. This method offers a powerful tool for political scientists and other researchers.
Original 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.
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