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This study introduces a unified Bayesian approach for statistically assessing causality. It treats causal statements as hypotheses, enabling robust analysis across diverse settings by computing posterior distributions.

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Area of Science:

  • Statistics
  • Causality
  • Bayesian Inference

Background:

  • Assessing causal relationships is challenging due to the lack of a universal definition of causality.
  • Statistical methods are crucial for evaluating causal links in various practical scenarios.

Purpose of the Study:

  • To present a uniform, general approach to causality problems grounded in the Bayesian statistical framework.
  • To frame causality statements as testable hypotheses within a probabilistic model.

Main Methods:

  • Utilizing the axiomatic foundations of Bayesian statistics.
  • Viewing causal statements as hypotheses (models) about the world.
  • Computing the posterior distribution of causal hypotheses given data and background knowledge.

Main Results:

  • Demonstrated a consistent framework for causality analysis applicable to diverse settings.
  • Showcased the computation of posterior distributions for causal hypotheses in illustrative examples.
  • Highlighted the integration of causality assessment within standard Bayesian modeling.

Conclusions:

  • The proposed Bayesian approach offers a unified and axiomatic foundation for causality assessment.
  • This method provides a consistent way to handle various causality problems, from specific to general cases.
  • The framework supports analyzing both causes of effects and effects of causes within a single methodology.