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Abstraction between Structural Causal Models: A Review of Definitions and Properties

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arxiv 2207.08603 v1 pith:6Z6O5HHS submitted 2022-07-18 cs.AI cs.LG

classification cs.AIcs.LG
keywords abstractioncausalpropertiesscmsallowsstructuraldifferentdistinguish
verification ladder T0 review T1 audit T2 compute T3 formal
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Structural causal models (SCMs) are a widespread formalism to deal with causal systems. A recent direction of research has considered the problem of relating formally SCMs at different levels of abstraction, by defining maps between SCMs and imposing a requirement of interventional consistency. This paper offers a review of the solutions proposed so far, focusing on the formal properties of a map between SCMs, and highlighting the different layers (structural, distributional) at which these properties may be enforced. This allows us to distinguish families of abstractions that may or may not be permitted by choosing to guarantee certain properties instead of others. Such an understanding not only allows to distinguish among proposal for causal abstraction with more awareness, but it also allows to tailor the definition of abstraction with respect to the forms of abstraction relevant to specific applications.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

    cs.LG 2026-08 conditional novelty 6.0 of 10

    CLAM jointly learns a shared subregional mechanism and a spatial disaggregation from aggregated outcomes, recovering a subregional treatment effect (LOCATE) from coarse data.

  2. Factored space models: Towards causality between levels of abstraction

    cs.AI 2024-12 conditional novelty 5.0 of 10

    Structural independence in a factored space is equivalent to conditional independence in all product distributions, generalizing d-separation to deterministic functions.

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