# Distribution Shift in Airline Customer Behavior during COVID-19

arXiv: https://arxiv.org/abs/2111.14938 · HTML: https://mlsanity.com/paper/2111.14938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Dynamic Pricing for Airline Ancillaries with Customer Context](https://mlsanity.com/paper/1902.02236/) (2019)

## Built on by (most cited first, 3 of 3)

- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift](https://mlsanity.com/paper/2211.12578/) (2022)
- [From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection](https://mlsanity.com/paper/2412.11308/) (2024)

## Abstract (arXiv)

Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice because of the dynamic nature of customer buying patterns, particularly due to unanticipated system shocks such as COVID-19. We study the changes in customer behavior for a major airline during the COVID-19 pandemic by framing it as a covariate shift and concept drift detection problem. We identify which customers changed their travel and purchase behavior and the attributes affecting that change using (i) Fast Generalized Subset Scanning and (ii) Causal Forests. In our experiments with simulated and real-world data, we present how these two techniques can be used through qualitative analysis.