Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs

Key points

• Historically, causal discovery has relied primarily on conditional independence.1
• This review focuses on modern causal discovery methods that exploit properties of distributions beyond conditional independence.1
• This perspective connects conditional-independence methods with distributional asymmetries, conditional-moment restrictions, and variation across environments or individuals.1
• We examine discovery under latent confounding and feedback, distinguishing causal information about observed variables from reconstruction of latent structure and emphasizing the additional semantics required by cyclic models.1

Sources (1)

• [1]Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 02:37 PM

  Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions.

  Historically, causal discovery has relied primarily on conditional independence.

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