arXiv 22 Sep 2025 · Statistics — Machine Learning
arXiv:2509.18047 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting for inter-individual heterogeneity in panel choice data. We identify three specific advantages of the Functional Effects Model over traditional fixed, and random/mixed effects models: (i) by mapping individual-specific effects as a function of socio-demographic variables, we can account for these effects when forecasting choices of previously unobserved individuals (ii) the (approximate) maximum-likelihood estimation of functional effects avoids the incidental parameters problem of the fixed effects model, even when the number of observed choices per individual is small; and (iii) we do not rely on the strong distributional assumptions of the random effects model, which may not match reality. We learn functional intercept and functional slopes with powerful non-linear machine learning regressors for tabular data, namely gradient boosting decision trees and deep neural networks. We validate our proposed methodology on a synthetic experiment and three real-world panel case studies, demonstrating that the Functional Effects Model: (i) can identify the true values of individual-specific effects when the data generation process is known; (ii) outperforms both state-of-the-art ML choice modelling techniques that omit individual heterogeneity in terms of predictive performance, as well as traditional static panel choice models in terms of learning inter-individual heterogeneity. The results indicate that the FI-RUMBoost model, which combines the individual-specific constants of the Functional Effects Model with the complex, non-linear utilities of RUMBoost, performs marginally best on large-scale revealed preference panel data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Salvadé, N., Hillel, T (2025) Rumboost: Gradient boosted random utility models self | 1.000 | 7 | 4 | 100% |
| 2 | Han, Y., Pereira, F.C., Ben-Akiva, M., Zegras, C (2022) A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability | 0.928 | 4 | 3 | 100% |
| 3 | Sifringer, B., Lurkin, V., Alahi, A (2020) Enhancing discrete choice models with representation learning | 0.737 | 3 | 2 | 100% |
| 4 | Krueger, R., Bierlaire, M., Daziano, R.A., Rashidi, T.H., Bansal, P (2021) Evaluating the predictive abilities of mixed logit models with unobserved inter-and intra-individual heterogeneity | 0.644 | 2 | 2 | 100% |
| 5 | Shi, X., Cao, W., Raschka, S (2023) Deep neural networks for rank-consistent ordinal regression based on conditional probabilities | 0.511 | 2 | 2 | 50% |
| 6 | Greene, W (2015) Panel data models for discrete choice | 0.511 | 2 | 1 | 100% |
| 7 | Xiong, Y., Kim, H.J., Singh, V (2019) Mixed effects neural networks (menets) with applications to gaze estimation | 0.511 | 2 | 1 | 100% |
| 8 | Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky,… (2024) Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation | 0.405 | 1 | 1 | 100% |
| 9 | Sigrist, F (2023) Latent gaussian model boosting | 0.405 | 1 | 1 | 100% |
| 10 | Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M (2019) Optuna: A next-generation hyperparameter optimization framework | 0.405 | 1 | 1 | 100% |
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