Causality and Scientific Inquiry: Lessons from Space Physics and Medical Sciences

TL;DR

This paper emphasizes the importance of applied mathematics in causal scientific inquiry, illustrating how it complements statistical methods to improve research rigor and reliability across sciences.

Contribution

It highlights the hazards of neglecting mathematical models in causality studies and demonstrates how they differentiate mechanistic and difference-making causal facets.

Findings

Applied mathematics clarifies causal mechanisms in space physics and medicine.

Understanding causal facets can explain research discrepancies.

Mathematical models enhance scientific rigor and reliability.

Abstract

Over the past two decades, the rapid surge in data-intensive computational techniques for statistical modeling may have had the effect of diminishing the use of applied mathematics in causal scientific inquiry. In this paper, co-authored by an astrophysicist, a mathematician, and philosophers, we assess the hazards of neglecting the branch of mathematics that constructs models to address causal questions in favor of statistical modeling alone. Causality is relevant in all branches of science and is often elucidated through applied mathematics. Here, we illuminate the idea with examples drawn from space physics and medical sciences. We examine causal questions to demonstrate how applied mathematical and statistical methods may differentiate between two fundamental facets of causality, i.e., mechanistic and difference-making. Understanding such foundational differences in causality may, in some cases, help explain discrepant or erroneous research results. Most importantly, understanding the relationship between causality and analytical approaches used in science has the potential to strengthen the rigor and reliability of scientific inquiry through optimal selection of mathematical and/or statistical methods.