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Unification and explanation from a causal perspective.

Alexander Gebharter1, Christian J Feldbacher-Escamilla2

  • 1Center for Philosophy, Science, and Policy (CPSP), Department of Biomedical Sciences and Public Health, Faculty of Medicine and Surgery, Marche Polytechnic University, Via Tronto 10/B, Ancona 60126, Italy.

Studies in History and Philosophy of Science
|March 26, 2023
PubMed
Summary

This study compares mutual information unification (MIU) and common origin unification (COU) measures. Causal constraints improve unification measures, but they ultimately fail to track explanatory relevance, challenging philosophical assumptions.

Keywords:
CausationExplanationUnification

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Area of Science:

  • Philosophy of Science
  • Causal Inference
  • Information Theory

Background:

  • Two prominent theories of scientific unification are mutual information unification (MIU) and common origin unification (COU).
  • Existing probabilistic measures for MIU and COU have limitations in capturing causal relationships.

Purpose of the Study:

  • To propose a novel probabilistic measure for common origin unification (COU).
  • To compare the proposed COU measure with existing mutual information unification (MIU) measures.
  • To evaluate the performance of these unification measures in simple and complex causal settings, particularly in relation to explanatory power.

Main Methods:

  • Development of a simple probabilistic measure for common origin unification (COU).
  • Comparison of the proposed COU measure with Myrvold's probabilistic measure for mutual information unification (MIU).
  • Assessment of both measures in simple causal structures and with added complexity, evaluating against explanatory power.

Main Results:

  • The proposed causal version of COU shows initial promise in simple causal settings compared to MIU.
  • Both causally constrained unification measures exhibit deficiencies and can diverge from explanatory power as causal complexity increases.
  • Sophisticated causally constrained unification measures do not consistently align with explanatory relevance.

Conclusions:

  • Unification and explanation are not as tightly linked as previously assumed in philosophical accounts.
  • Current measures of unification, even when causally constrained, may not adequately capture the essence of scientific explanation.
  • Further research is needed to reconcile measures of unification with the principles of causal explanation.