Continual Causal Abstractions
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Abstract (arXiv)
This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 23 Dec 2022.
Builds on
11 of 14 references.
References · 11
- 2021Towards Causal Representation Learningcited by 207
- 2017Causal Consistency of Structural Equation Modelscited by 114
- 2018Abstracting Causal Modelscited by 104
- 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
- 2020CASTLE: Regularization via Auxiliary Causal Graph Discoverycited by 41
- 2022Causal Conceptions of Fairness and their Consequencescited by 38
- 2021CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparabilitycited by 28
- 2019Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognitioncited by 26
- 2021Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Modelscited by 22
- 2020Learning Causal Models Onlinecited by 18
- 2021A Taxonomy for Inference in Causal Model Familiescited by 1
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