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Cause or Trigger? From Philosophy to Causal Modeling

Kateřina Hlaváčková-Schindler, Rainer Wöß, Vera Pecorino, Philip Schindler

TL;DR

Addresses distinguishing triggers from causes in causal reasoning for natural systems and provides a formal framework to quantify both within causal mechanisms. Introduces a Cause-Trigger algorithm, grounded in philosophical distinctions and time-series analysis, and demonstrates its applicability to cyclogenesis using ERA5 data for Cyclones Freddy and Zazu. The algorithm identifies triggering variables, notably wind speed and the sine of wind direction, across pressure levels, with occasional involvement of ozone and humidity, aligning with physical cyclone dynamics. This data-driven tool enables earlier detection of triggering processes and supports informed decision-making in climate risk management and emergency planning.

Abstract

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.

Cause or Trigger? From Philosophy to Causal Modeling

TL;DR

Addresses distinguishing triggers from causes in causal reasoning for natural systems and provides a formal framework to quantify both within causal mechanisms. Introduces a Cause-Trigger algorithm, grounded in philosophical distinctions and time-series analysis, and demonstrates its applicability to cyclogenesis using ERA5 data for Cyclones Freddy and Zazu. The algorithm identifies triggering variables, notably wind speed and the sine of wind direction, across pressure levels, with occasional involvement of ozone and humidity, aligning with physical cyclone dynamics. This data-driven tool enables earlier detection of triggering processes and supports informed decision-making in climate risk management and emergency planning.

Abstract

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.

Paper Structure

This paper contains 16 sections, 9 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Causality in philosophy
  3. Cause as the supply of energy
  4. Cause as the trigger (releasing factor)
  5. Cause as the supply of structural information
  6. Quantification of cause and trigger among physical processes
  7. Required properties of the cause-trigger model
  8. Cause-Trigger algorithm
  9. Details to Cause-Trigger Algorithm
  10. Detection of triggers in cyclones by the Cause-Trigger algorithm
  11. Experiments
  12. Data set and variables
  13. Cause-Trigger algorithm in 2D spatio-temporal grids
  14. Spatio-temporal grids for three pressure levels for Freddy and Zazu and their physical interpretation
  15. Discussion and conclusions
  16. ...and 1 more sections

Figures (4)

  • Figure 1: (a) Volcano Etna in February 2021; (b) Cyclone Freddy approaching Madagascar on February, 21st, 2023. Drawings made by the first author based on the fotos in public domain.
  • Figure 2: Example of wind-related time-series at one of the 64 locations of Freddy. The goal of these experiments was to find out in which interval a good separation by detecting the maximal difference of means index can be obtained.
  • Figure 3: Triggering variables detected by Cause-Trigger algorithm per location and pressure-level for both cyclones. Longitude and latitude are shown on the x- and y-axis, respectively. Each color corresponds to a triggering variable.
  • Figure 4: 3D plots of cyclones Freddy and Zazu: Longitude and latitude are shown on the x- and y-axis, respectively. The z- axis displays the height in km with respect to the sea surface level. We present only location where triggering variables are active. Each colored cube corresponds to a triggering variable.

Theorems & Definitions (2)

  • Remark 1
  • Remark 2