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Summary
This summary is machine-generated.

This study reviews computational methods for causal discovery, enabling scientists to uncover cause-and-effect relationships from observational data when experiments are not feasible. It covers key techniques developed over the last 30 years.

Keywords:
causal discoveryconditional independencedirected graphical causal modelsnon-Gaussian distributionnon-linear modelsstatistical independencestructural equation models

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Area of Science:

  • Computational biology
  • Statistical inference
  • Causal inference

Background:

  • Identifying causal relationships is crucial across scientific fields, but direct interventions are often impractical.
  • Observational data analysis offers an alternative for inferring causal structures.

Purpose of the Study:

  • To provide an introduction and review of computational methods for causal discovery.
  • To summarize techniques developed over the past three decades.

Main Methods:

  • Review of constraint-based methods for causal discovery.
  • Review of score-based methods for causal discovery.
  • Discussion of methods based on functional causal models.

Main Results:

  • Overview of established computational approaches for causal discovery.
  • Illustrations and applications of causal discovery methods are provided.

Conclusions:

  • Computational methods offer powerful tools for inferring causality from observational data.
  • The reviewed techniques provide a foundation for advancing causal discovery in science.