Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is vital to risk mitigation. The purpose of this paper is to perform data mining using causal machine learning algorithms in the USELEI process (Understanding, Sampling, Exploring, Learning, Evaluating, and Inferring) to predict the probability of flight delays in air transportation using data collected from different sources. The findings indicated significant effects of predictors, including reported arrivals and departures, arrival and departure demands, capacity, efficiency, and traffic volume at the origin and destination airports on the risk of flight delays. More importantly, causal interrelationships among variables in a fully structural network are presented to how these predictors interact with one another and how these interactions lead to delay incidents. Finally, sensitivity analysis and causal inference can be performed to evaluate various what-if scenarios and form effective strategies to mitigate the risk of delays.

航空运输的延迟是一个主要问题，对航空业和经济产生负面影响。鉴于国家航空航天系统的复杂性，预测飞行延误的风险并确定重要的预测因素对于降低风险至关重要。本文的目的是执行数据使用因果机器学习在USELEI处理算法矿业（Û nderstanding，小号ampling，ê xploring，大号收入，ë计价和我nferring）使用从不同来源收集的数据来预测航空运输中航班延误的可能性。调查结果表明，预测因素具有重大影响，包括所报告的进场和离场，进场和离场需求，容量，效率以及始发地和目的地机场的业务量对航班延误风险的影响。更重要的是，在一个完全结构化的网络中，变量之间的因果关系被呈现给这些预测变量如何相互影响以及这些相互作用如何导致延迟事件。最后，可以进行敏感性分析和因果推断，以评估各种假设情景，并形成减轻延迟风险的有效策略。