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[2505.14825] Assimilative Causal Inference
Summary
The paper presents Assimilative Causal Inference (ACI), a novel framework that utilizes Bayesian data assimilation to identify dynamic causal relationships in complex systems, addressing limitations of traditional causal inference methods.
Why It Matters
ACI offers a significant advancement in causal inference methodologies, particularly for high-dimensional and time-evolving systems. By enabling the identification of causal interactions without direct observations of causes, it opens new avenues for research in fields where understanding transient causal structures is crucial.
Key Takeaways
- ACI leverages Bayesian data assimilation to trace causes from observed effects.
- It uniquely identifies dynamic causal interactions without needing observations of candidate causes.
- The framework accommodates short datasets and can be applied in high-dimensional settings.
- ACI allows for online tracking of causal roles that may reverse intermittently.
- The methodology is demonstrated through complex dynamical systems showcasing intermittency.
Computer Science > Machine Learning arXiv:2505.14825 (cs) [Submitted on 20 May 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Assimilative Causal Inference Authors:Marios Andreou, Nan Chen, Erik Bollt View a PDF of the paper titled Assimilative Causal Inference, by Marios Andreou and 2 other authors View PDF HTML (experimental) Abstract:Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward from observed effects. ACI solves the inverse problem rather than quantifying forward influence. It uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and, in principle, can be implemented in high-dimensional settings by employing efficient data assimilation algorithms. Crucially, it provides online tracking of causal roles that may reverse intermittently and facilitates a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events. ACI opens valuable pathways for studying complex systems, where transient causal structures are critical. Comme...
