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Measuring Causal Invariance Formally.

Pierrick Bourrat1,2

  • 1Department of Philosophy, Macquarie University, Balaclava Road, North Ryde, NSW 2109, Australia .

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces two formal measures to quantify invariance, a key aspect of causal relationships. The research aims to improve the understanding and estimation of causal connections, particularly for non-nominal variables.

Keywords:
causal specificitycausationinformation theoryinvariance

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Area of Science:

  • Causal inference
  • Philosophy of science
  • Information theory

Background:

  • Invariance is a crucial dimension in the interventionist account of causality.
  • A more invariant relationship indicates a more paradigmatically causal connection between variables.

Purpose of the Study:

  • To propose two formal measures for estimating invariance in causal relationships.
  • To discuss and extend the concept of invariance to non-nominal variables (ordinal and quantitative).

Main Methods:

  • Development of two novel formal measures to quantify invariance.
  • Illustration of the proposed measures with a simple example.
  • Discussion of limitations of information theory for non-nominal variables.

Main Results:

  • Formalisms for estimating invariance are presented.
  • The applicability and limitations of the proposed methods for non-nominal variables are discussed.

Conclusions:

  • The proposed measures offer a quantitative approach to assessing causal invariance.
  • Further work is needed to adapt invariance concepts for ordinal and quantitative variables.