Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study
Qiao Kang, Xing Song, Xiaying Xin, Bing Chen, Yuanzhu Chen, Xudong Ye, Baiyu Zhang
Abstract
, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.