mlsanityData to 14 Oct 2026

Multi-Level Cause-Effect Systems

arXiv 1512.07942 ↗First posted 25 Dec 2015stat.ML cs.AIcited by 30

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Abstract (arXiv)

We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Dec 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 8; 2023: 6; 2024: 4; 2025: 4; 2026: 4.

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  1. 2021Causal Abstractions of Neural Networkscited by 422
  2. 2021Towards Causal Representation Learningcited by 209
  3. 2022CITRIS: Causal Identifiability from Temporal Intervened Sequencescited by 133
  4. 2021Inducing Causal Structure for Interpretable Neural Networkscited by 110
  5. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 106
  6. 2022From Statistical to Causal Learningcited by 40
  7. 2022Causal Effect Identification in Cluster DAGscited by 36
  8. 2022Causal Inference Through the Structural Causal Marginal Problemcited by 23
  9. 2022Image-based Treatment Effect Heterogeneitycited by 20
  10. 2023Causal models in string diagramscited by 14
  11. 2022On the Equivalence of Causal Models: A Category-Theoretic Approachcited by 13
  12. 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
18 more
  1. 2022Towards a Grounded Theory of Causation for Embodied AIcited by 9
  2. 2024Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confoundingcited by 9
  3. 2023Integrating Earth Observation Data into Causal Inference: Challenges and Opportunitiescited by 8
  4. 2023Meaningful Causal Aggregation and Paradoxical Confoundingcited by 5
  5. 2025Nonlinear Causal Discovery for Grouped Datacited by 5
  6. 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clusteringcited by 5
  7. 2021Encoding Causal Macrovariablescited by 4
  8. 2022Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africacited by 4
  9. 2024The Causal Information Bottleneck and Optimal Causal Variable Abstractionscited by 4
  10. 2026Coarsening Causal DAG Modelscited by 4
  11. 2023Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studiescited by 3
  12. 2024Causal reasoning in difference graphscited by 2
  13. 2025Causal DAG Summarization (Full Version)cited by 2
  14. 2023Do Not Marginalize Mechanisms, Rather Consolidate!cited by 1
  15. 2026Unsupervised Causal Abstractions Discoverycited by 1
  16. 2026Joint Causal Structure and Cluster Discovery Using Variational Inferencecited by 1
  17. 2025Causal Feature Learning in the Social Sciencescited by 0
  18. 2026Validating Causal Abstraction Metrics on Simulated Complex Systemscited by 0