Causality in Flux: Continual Adaptation of Causal Knowledge via Evidence Matching

Jonas Seng, Florian Peter Busch, Kristian Kersting
Proceedings of The Second AAAI Bridge Program on Continual Causality, PMLR 268:11-20, 2025.

Abstract

Utilising causal knowledge in machine learning (ML) systems yields more robust models with the capability of performing certain extrapolations. However, much of current causality research focuses on deriving causal models in isolation, hence current systems are not capable of updating and improving causal knowledge when new observations arrive. Drawing inspiration from human learning, Continual Learning (CL) aims at updating models given a sequential stream of evidence. Leveraging common patterns and past experiences to gradually improve causal knowledge in ML models is a crucial step towards more robust CL systems. In this work, we propose to learn and update causal models in a lifelong learning setting where causal knowledge explaining newly arriving observations is inferred from similar previously seen observations. We call this framework evidence matching. Further, an analysis of real world data supporting our motivation is provided.

Cite this Paper

BibTeX
@InProceedings{pmlr-v268-seng25a, title = {Causality in Flux: Continual Adaptation of Causal Knowledge via Evidence Matching}, author = {Seng, Jonas and Busch, Florian Peter and Kersting, Kristian}, booktitle = {Proceedings of The Second AAAI Bridge Program on Continual Causality}, pages = {11--20}, year = {2025}, editor = {Mundt, Martin and Cooper, Keiland W. and Dhami, Devendra Singh and Hayes, Tyler and Herman, Rebecca and Ribeiro, Adéle and Smith, James Seale}, volume = {268}, series = {Proceedings of Machine Learning Research}, month = {20--21 Feb}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v268/main/assets/seng25a/seng25a.pdf}, url = {https://proceedings.mlr.press/v268/seng25a.html}, abstract = {Utilising causal knowledge in machine learning (ML) systems yields more robust models with the capability of performing certain extrapolations. However, much of current causality research focuses on deriving causal models in isolation, hence current systems are not capable of updating and improving causal knowledge when new observations arrive. Drawing inspiration from human learning, Continual Learning (CL) aims at updating models given a sequential stream of evidence. Leveraging common patterns and past experiences to gradually improve causal knowledge in ML models is a crucial step towards more robust CL systems. In this work, we propose to learn and update causal models in a lifelong learning setting where causal knowledge explaining newly arriving observations is inferred from similar previously seen observations. We call this framework evidence matching. Further, an analysis of real world data supporting our motivation is provided.} }
Endnote
%0 Conference Paper %T Causality in Flux: Continual Adaptation of Causal Knowledge via Evidence Matching %A Jonas Seng %A Florian Peter Busch %A Kristian Kersting %B Proceedings of The Second AAAI Bridge Program on Continual Causality %C Proceedings of Machine Learning Research %D 2025 %E Martin Mundt %E Keiland W. Cooper %E Devendra Singh Dhami %E Tyler Hayes %E Rebecca Herman %E Adéle Ribeiro %E James Seale Smith %F pmlr-v268-seng25a %I PMLR %P 11--20 %U https://proceedings.mlr.press/v268/seng25a.html %V 268 %X Utilising causal knowledge in machine learning (ML) systems yields more robust models with the capability of performing certain extrapolations. However, much of current causality research focuses on deriving causal models in isolation, hence current systems are not capable of updating and improving causal knowledge when new observations arrive. Drawing inspiration from human learning, Continual Learning (CL) aims at updating models given a sequential stream of evidence. Leveraging common patterns and past experiences to gradually improve causal knowledge in ML models is a crucial step towards more robust CL systems. In this work, we propose to learn and update causal models in a lifelong learning setting where causal knowledge explaining newly arriving observations is inferred from similar previously seen observations. We call this framework evidence matching. Further, an analysis of real world data supporting our motivation is provided.
APA
Seng, J., Busch, F.P. & Kersting, K.. (2025). Causality in Flux: Continual Adaptation of Causal Knowledge via Evidence Matching. Proceedings of The Second AAAI Bridge Program on Continual Causality, in Proceedings of Machine Learning Research 268:11-20 Available from https://proceedings.mlr.press/v268/seng25a.html.

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