Causal emergence is defined by the difference in the EI values between the macro-level and micro-level.

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• Causal emergence is widespread across measures of causation, not just EI.claim0.880
  Claim by Comolatti & Hoel (2022) endorsed by this survey.
• Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.claim0.848
  Cross-fertilization claim made in discussion.
• Causal emergence depends on the coarse-graining strategy: different partitions of the same boolean network yield EI values 1.55 (emergence) vs 0.18 (degradation).finding0.847
  Example from Hoel et al. (2013) replicated in the survey.
• Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.843
  Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
• Emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties.quote0.836
  Load-bearing definition from the abstract.
• Causal Emergenceconcept0.835
  Core concept: degree to which an agent exerts unique predictive power on its future; key to cognition at all scales.
• Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.833
  Insight that coarse-graining filters external noise but not intrinsic noise.
• Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.816
  Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.