mlsanityData to 14 Oct 2026

Causal Explanations Over Time: Articulated Reasoning for Interactive Environments

arXiv 2506.03915 ↗First posted 4 Jun 2025cs.AIcited by 0

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Abstract (arXiv)

Structural Causal Explanations (SCEs) can be used to automatically generate explanations in natural language to questions about given data that are grounded in a (possibly learned) causal model. Unfortunately they work for small data only. In turn they are not attractive to offer reasons for events, e.g., tracking causal changes over multiple time steps, or a behavioral component that involves feedback loops through actions of an agent. To this end, we generalize SCEs to a (recursive) formulation of explanation trees to capture the temporal interactions between reasons. We show the benefits of this more general SCE algorithm on synthetic time-series data and a 2D grid game, and further compare it to the base SCE and other existing methods for causal explanations.

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References · 16

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4 more
  1. 2019CXPlain: Causal Explanations for Model Interpretation under Uncertaintycited by 98
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  3. 2021CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparabilitycited by 28
  4. 2021Causal Explanations of Structural Causal Modelscited by 4

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