A Counterfactual Framework Based on the Machine Learning Method and Its Application to Measure the Impact of COVID-19 Local Outbreaks on the Chinese Aviation Market

Abstract

COVID-19 affects aviation around the world. China’s civil aviation almost recovered to its pre-epidemic levels in the domestic market, but there are still local outbreaks that affect air traffic. This paper proposes measuring the impact of local outbreaks of COVID-19 by the machine learning method and the synthetic control method as a counterfactual control group to measure such an impact. In this study, we use the LightGBM algorithm to construct a counterfactual control group and transform the prediction problem from time series to the fitting problem at the spatial level. We find that machine learning methods can measure such an impact more accurately. We take local outbreaks in Beijing and Dalian as examples, and our measure of their impacts shows that the impact of an outbreak on intercity air traffic can be divided into lag, decline, stable, and recovery periods, and will last for a long period (more than 40 days) unless there are external stimuli, such as legal holidays. The outbreaks reduced the number of passengers in the cities by 90%. Finally, we show the impact on the air traffic network, and find that when a local outbreak happens in a big city, tourist cities or small stations will be greatly affected.