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[2510.16703] On the Granularity of Causal Effect Identifiability
Summary
This paper explores the concept of causal effect identifiability, focusing on state-based effects and how they can be identifiable even when variable-based effects are not, given certain contextual knowledge.
Why It Matters
Understanding causal effect identifiability is crucial for advancing methodologies in machine learning and artificial intelligence. This paper contributes to the field by highlighting the significance of context-specific knowledge in improving identifiability, which can enhance decision-making processes in various applications.
Key Takeaways
- State-based causal effects can be identifiable even when variable-based effects are not.
- Context-specific independencies play a critical role in causal effect identifiability.
- Additional knowledge can improve both variable-based and state-based identifiability.
- The paper proposes a new approach for identifying causal effects under constraints.
- Empirical studies illustrate the differences between state-based and variable-based identifiability.
Computer Science > Machine Learning arXiv:2510.16703 (cs) [Submitted on 19 Oct 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:On the Granularity of Causal Effect Identifiability Authors:Yizuo Chen, Adnan Darwiche View a PDF of the paper titled On the Granularity of Causal Effect Identifiability, by Yizuo Chen and 1 other authors View PDF HTML (experimental) Abstract:The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the identifiability of state-based causal effects: how an intervention on a particular state of treatment variables affects a particular state of outcome variables. We demonstrate that state-based causal effects may be identifiable even when variable-based causal effects may not. Moreover, we show that this separation occurs only when additional knowledge -- such as context-specific independencies -- is available. We further examine knowledge that constrains the states of variables, and show that such knowledge can improve both variable-based and state-based identifiability when combined with other knowledge such as context-specific independencies. We finally propose an approach for identifying causal effects under these additional constraints, and conduct empirical studies to further illustrate the separations between the two levels of identifiability. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME) Cite as: arXiv:2510...
