Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on characterizations that differentiate them from each other. We carry out a philosophical analysis of these differences. From this, we formulate a definition that clearly differentiates triggers from causes and can be used for causal reasoning in natural sciences. We propose a mathematical model and the Cause-Trigger algorithm, which, based on given data to observable processes, is able to determine whether a process is a cause or a trigger of an effect. The possibility to distinguish triggers from causes directly from data makes the algorithm a useful tool in natural sciences using observational data, but also for real-world scenarios. For example, knowing the processes that trigger causes of a tropical storm could give politicians time to develop actions such as evacuation the population. Similarly, knowing the triggers of processes that cause global warming could help politicians focus on effective actions. We demonstrate our algorithm on the climatological data of two recent cyclones, Freddy and Zazu. The Cause-Trigger algorithm detects processes that trigger high wind speed in both storms during their cyclogenesis. The findings obtained agree with expert knowledge.

翻译：在因果推理、因果建模或哲学文献中，关于诱因作用的论述并不多见。本文聚焦于从形而上学意义上描述诱因与原因，并阐述区分二者的特征。我们对这些差异进行了哲学分析。基于此，我们提出了一个能明确区分诱因与原因的定义，该定义可用于自然科学中的因果推理。我们提出了一个数学模型及Cause-Trigger算法，该算法基于可观测过程的给定数据，能够判定一个过程是某个效应的原因还是诱因。直接从数据中区分诱因与原因的能力，使得该算法不仅成为利用观测数据的自然科学中的有用工具，也适用于现实世界场景。例如，了解触发热带风暴成因的过程，可以为决策者提供时间制定诸如疏散人口等行动方案。同样，了解导致全球变暖过程的触发因素，有助于决策者聚焦于有效的行动。我们在近期两个气旋Freddy和Zazu的气候数据上验证了我们的算法。Cause-Trigger算法检测到了在两个风暴气旋生成期间触发高风速的过程。所得结果与专家知识相符。