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This research advances causal inference in machine learning by demonstrating that state-based interventions can be identifiable even when traditional variable-level effects are not. The authors show how incorporating context-specific independencies and constraints on variable states significantly improves identifiability, offering a refined framework for complex AI reasoning tasks. This work provides critical theoretical tools for building more robust models capable of handling nuanced causal structures beyond standard binary treatments.
On the Granularity of Causal Effect Identifiability
arXiv:2510.16703v3 Announce Type: replace-cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME)
arXiv:2510.16703 [cs.LG]
(or arXiv:2510.16703v3 [cs.LG] for this version)
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