Assessing the Continuous Causal Responses of Typhoon-related Weather on Human Mobility: An Empirical Study in Japan

To understand human mobility following the typhoon, analyzing the causal impact of extreme typhoon weather on human mobility is important for disaster emergency management. However, the unobserved confounders (e.g., the characteristic of each region) correlate with the strength of typhoon weather and also affect human mobility during typhoon, which may generate biased influences on the causal analysis process. Besides, these confounders may be time-varying following the dynamic movements of typhoon. In this work, we develop a neural network-based continuous causal effect estimation framework to mitigate the interference from (unobserved) confounders and assess the continuous causal responses of typhoon-related weather (treatment) on several types of human mobility (outcome) across different counties at any given period. To this end, we integrate the big data from two huge typhoons in Japan (i.e., Typhoon Faxai and Hagibis) and leverage multiple sources of covariates (i.e., residents' vigilance and basic mobility patterns) from different counties to learn the representations of time-varying confounders. The experimental results indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of extreme weather during the typhoon process, compared with several existing causal studies.