Greta Laage, Emma Frejinger, Andrea Lodi, Guillaume Rabusseau
arXiv 13 Jan 2021 · Machine Learning · 1 citations (OpenAlex)
arXiv:2101.10249 · PDF · DOI · OpenAlex · Extracted main text
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before. The latter is not observable. Moreover, the expected impact can be small in relative value. In this paper, we cast the problem as counterfactual prediction of unobserved revenue. The impact on revenue is then the difference between the observed and the estimated revenue. The originality of this work lies in the innovative application of econometric methods proposed for macroeconomic applications to a new problem setting. Broadly applicable, the approach benefits from only requiring revenue data observed for origin-destination pairs in the network of the airline at each day, before and after a change in the system is applied. We report results using real large-scale data from Air Canada. We compare a deep neural network counterfactual predictions model with econometric models. They achieve respectively 1% and 1.1% of error on the counterfactual revenue predictions, and allow to accurately estimate small impacts (in the order of 2%).
appendix boundary found by appendix_titled_section at “Appendix” · 94% of the source is main text. Read the extracted text to check this.
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., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2018) Matrix Completion Methods for Causal Panel Data Models, 2018 | 1.000 | 11 | 3 | 100% |
| 2 | Doudchenko, N. and Imbens, G. W (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis | 1.000 | 7 | 3 | 100% |
| 3 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 1.000 | 5 | 3 | 100% |
| 4 | Abadie, A. and Gardeazabal, J (2003) The economic costs of conflict: A case study of the Basque Country | 0.928 | 4 | 3 | 100% |
| 5 | Amjad, M., Shah, D., and Shen, D (2018) Robust synthetic control | 0.874 | 5 | 2 | 100% |
| 6 | Mazumder, R., Hastie, T., and Tibshirani, R (2010) Spectral regularization algorithms for learning large incomplete matrices | 0.811 | 4 | 2 | 100% |
| 7 | Ashenfelter, O. and Card, D (1985) Using the longitudinal structure of earnings to estimate the effect of training programs | 0.644 | 2 | 2 | 100% |
| 8 | Athey, S. and Imbens, G. W (2006) Identification and inference in nonlinear difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 9 | Card, D (1990) The impact of the Mariel boatlift on the Miami labor market | 0.644 | 2 | 2 | 100% |
| 10 | Card, D. and Krueger, A. B (1994) Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 37 scored citations.