The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries . Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes . Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied . The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment . In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers . The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted . Results show the feasibility and scalability of this model . On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22 , 2020, would be spent for the number of commercial flights to recover back to 58.52% -60.39% on different interventions . This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport.