Proof and Uncertainty in Causal Claims

  • Martine Jayne Barons University of Warwick
  • Rachel L Wilkerson University of Warwick

Abstract

Causal questions drive scientific enquiry. From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms. The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models. We explore the relationship between existing theories about causal mechanisms in a social science domain, new mathematical and statistical modelling methods, the role of mathematical proof and the importance of accounting for uncertainty. We show that, while the mathematical sciences rely on their modelling assumptions, dialogue with the social sciences calls for continual extension of these models. We show how changing model assumptions lead to innovative causal structures and more nuanced casual explanations. We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics.

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Author Biographies

Martine Jayne Barons, University of Warwick

Dr. Martine J. Barons

Director of the Applied Statistics & Risk Unit, Department of Statistics

Rachel L Wilkerson, University of Warwick

Department aStatisics, PhD student

Published
2018-06-07
How to Cite
BARONS, Martine Jayne; WILKERSON, Rachel L. Proof and Uncertainty in Causal Claims. Exchanges: The Interdisciplinary Research Journal, [S.l.], v. 5, n. 2, p. 72-89, june 2018. ISSN 2053-9665. Available at: <https://exchanges.warwick.ac.uk/article/view/238>. Date accessed: 14 aug. 2018.
Section
Featured Theme: Truth & Evidence