The global air transport industry, an integral component of the world economy, has faced an unprecedented economic crisis characterized by a substantial reduction in demand. This reduction is more intense than any previous recessions the industry has encountered, and it is primarily attributed to the impact of the COVID-19 pandemic and the stringent travel restrictions that followed in its wake. This study explores the causal relationship between air transport demand and pertinent socio-economic indicators, with the aim of quantifying the future growth prospects of the air transport sector. It places particular emphasis on understanding the dynamics of recovery after recessions, regardless of their underlying causes, scope, or duration. Drawing upon a dataset spanning from 1990 to 2022, which encompasses periods of significant recessions that have left a notable imprint on the air transport industry, this research meticulously examines the causal links between passenger air transport indicators, other key transportation metrics, and a diverse array of socio-economic indicators that wield substantial influence over economic trends during post-recession recovery phases. The findings of this study reveal the existence of discernible causal relationships between air transport indicators and socio-economic indicators. However, these causal relationships exhibit a dynamic nature, manifesting variations across different observed periods. Notably, during recessions, specific socio-economic indicators exert a more pronounced influence on air transportation metrics. In contrast, during economic expansion periods, alternative socio-economic factors come to the fore, indicating a shifting landscape of causality that necessitates adaptive strategies. To facilitate precise forecasting of passenger air traffic recovery, this research introduces a temporal causal model. This model represents a systematic approach that integrates various transportation and socio-economic indicators while considering their causal relationships. It strategically selects predictors that exhibit significant impacts on the chosen air transport indicators, thereby enhancing the accuracy of forecasts and providing decision-makers with actionable insights. Using the time-causal model, a system of time-series has been created, allowing for the identification of causal relationships between selected time-series at a much more complex level than the bivariate Granger causality test. The time-causal model includes the use of autoregressive models, vector autoregressive models (VAR), vector error correction models (VECM), and the Granger causality test. Air transport indicators are assigned the role of predictor and target variables simultaneously, while socio-economic indicators are assigned the role of predictor variables. It was determined that there is a difference in causal relationships during periods of expansion and recession. During recession periods, indicators such as the price of crude oil, jet fuel, and ownership rates have a stronger influence on air transport indicators, whereas during expansion periods, the number of employees in the air transport industry and airline ticket prices have a stronger influence on air transport indicators. Some socio-economic indicators, such as population and gross domestic product, affect air transport indicators in both expansion and recession periods, although not necessarily with the same intensity. Forecasted values from the temporal causal and ARIMA models were compared with realized traffic to assess the quality and accuracy of the models. Based on the comparison the time-causal model enables instant analysis of circumstances and causes in dynamic environments, as well as reliable forecasting of upcoming periods. In the aftermath of economic downturns, airlines face the formidable challenge of recovery. Building on empirical evidence and rigorous analysis, this research culminates in the formulation of pragmatic guidelines. These guidelines provide a structured framework for airlines to navigate the turbulent post-recession landscape more effectively. By optimizing operational processes, refining marketing strategies, and fostering collaboration among industry stakeholders, these recommendations aim to expedite and fortify the recovery process. This comprehensive study sheds light on the intricate dynamics within the global air transport industry during times of crisis and recovery. It equips industry stakeholders with a powerful forecasting tool, the temporal causal model, to make informed decisions in an ever-changing landscape. By unraveling the multifaceted causal relationships and offering practical recovery guidelines, this research contributes significantly to the body of knowledge in the field of air transportation economics and management. The acquired insights pave the way for informed decision-making and sustainable growth in the industry.