arXiv:2505. 15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making.
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arXiv:2608. 12657v1 Announce Type: new Abstract: Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification.
The paper presents a method for partially identifying counterfactual queries without requiring a fully specified causal graph. By exploiting the topological ordering implied by the query itself, the authors transform the identification problem into a linear programming task, enabling bounds on arbitrary counterfactual and nested counterfactual queries. They demonstrate the tightness of these bounds and illustrate the approach on several case studies, showing its usefulness even when causal knowledge is incomplete.
The paper introduces causal foundation models that can bound the effects of interventions and counterfactuals using only observational data. It defines a canonical prior with full support over structural causal models with discrete observables, enabling the translation of counterfactual bounding into learning distributions over functions that map data and structural assumptions to causal queries. This approach extends causal foundational modelling to partially-identifiable causal effects, where unobserved confounding leads to multiple compatible values for the effect.
arXiv:2607. 16717v1 Announce Type: cross Abstract: This paper proposes a causal independence principle for value -- the value Causal Markov Condition (v-CMC) -- and develops the conceptual and mathematical foundations of a "causal value theory" linking causality and utility.
arXiv:2606. 29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur.
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.