Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva
arXiv 21 Jan 2021 · Machine Learning · 3 citations (OpenAlex)
arXiv:2101.10261 · PDF · DOI · OpenAlex · Extracted main text
This paper discusses capabilities that are essential to models applied in policy analysis settings and the limitations of direct applications of off-the-shelf machine learning methodologies to such settings. Traditional econometric methodologies for building discrete choice models for policy analysis involve combining data with modeling assumptions guided by subject-matter considerations. Such considerations are typically most useful in specifying the systematic component of random utility discrete choice models but are typically of limited aid in determining the form of the random component. We identify an area where machine learning paradigms can be leveraged, namely in specifying and systematically selecting the best specification of the random component of the utility equations. We review two recent novel applications where mixed-integer optimization and cross-validation are used to algorithmically select optimal specifications for the random utility components of nested logit and logit mixture models subject to interpretability constraints.
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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 | Athey, S (2018) The impact of machine learning on economics | 0.843 | 3 | 3 | 100% |
| 2 | Ben-Akiva, M. E. and S. R. Lerman (1985) Discrete choice analysis: theory and application to travel demand, Volume 9 self | 0.811 | 4 | 2 | 100% |
| 3 | Manski, C. F (2009) Identification for prediction and decision | 0.811 | 4 | 2 | 100% |
| 4 | Aboutaleb, Y. M (2019) Learning structure in nested logit models self | 0.737 | 3 | 2 | 100% |
| 5 | Aboutaleb, Y. M., M. Danaf, Y. Xie, and M. Ben-Akiva (2021) Sparse covariance estimation in logit mixture models self | 0.737 | 3 | 2 | 100% |
| 6 | Manski, C. F (2013) Public policy in an uncertain world: analysis and decisions | 0.644 | 2 | 2 | 100% |
| 7 | McFadden, D (1981) Econometric models of probabilistic choice | 0.644 | 2 | 2 | 100% |
| 8 | Pearl, J (2000) Causality: models, reasoning and inference, Volume 29 | 0.644 | 2 | 2 | 100% |
| 9 | Train, K. E (2009) Discrete choice methods with simulation | 0.644 | 2 | 2 | 100% |
| 10 | Rosasco, L. and T. Poggio (2017, December) (2017) Machine learning: a regularization approach mit-9.520 lectures notes | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 44 scored citations.