Cause or Trigger? From Philosophy to Causal Modeling
This work addresses a gap in causal reasoning for natural sciences and real-world scenarios, such as predicting tropical storms or global warming triggers, but it is incremental as it builds on existing philosophical and causal modeling concepts.
The paper tackles the problem of distinguishing triggers from causes in causal modeling by proposing a mathematical model and the Cause-Trigger algorithm, which detects triggers for effects like high wind speed in cyclones with findings that agree with expert knowledge.
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.