claim

active

claim:causal-emergence-identification-tasks-can-be-understood-as-causal-representation-learning-tasks

Causal emergence identification tasks can be understood as causal representation learning tasks.

Authors propose a conceptual mapping between CE identification and CRL.

Source paper

extracted_from

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

(2023) · Bing Yuan · Jiang Zhang · Aobo Lyu · Jiayun Wu +5

Neighborhood — ranked by edge-count

Communities (3)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning agents
members_of
Uses effective information (EI) and coarse-graining to link causal emergence with RL and biological learning.
Causal emergence in learning and adaptation
members_of
Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Frameworks (1)

framework

Causal Representation Learning (CRL)
associated_with
Schölkopf et al.'s framework combining representation learning with causal inference.

Concepts (1)

concept

Causal Emergence
associated_with
Core concept: degree to which an agent exerts unique predictive power on its future; key to cognition at all scales.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.claim0.873
Cross-fertilization claim made in discussion.
Representational dynamics of causal emergence align with reward improvement in most tasks.finding0.865
The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.
Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.828
Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.finding0.827
Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.820
Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
Causal emergence is defined by the difference in the EI values between the macro-level and micro-level.quote0.814
Core definition from §1.
successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.798
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.
Causal emergence can enable causal interventions to create better RL agents.claim0.797
Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.