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[2410.19412] VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery
Summary
The paper presents VCDF, a consensus-driven framework for enhancing the robustness of time series causal discovery, improving stability and accuracy without altering existing algorithms.
Why It Matters
Time series causal discovery is crucial for understanding dynamic systems across various fields. VCDF addresses common challenges such as noise and non-stationarity, making it a significant advancement for researchers and practitioners relying on accurate causal inference.
Key Takeaways
- VCDF improves causal discovery stability across temporal subsets.
- It enhances existing algorithms like VAR-LiNGAM without modification.
- The framework shows significant performance gains on long time series.
- Experiments indicate improved accuracy under realistic noise conditions.
- VCDF is applicable across various domains, including finance and neuroscience.
Computer Science > Machine Learning arXiv:2410.19412 (cs) [Submitted on 25 Oct 2024 (v1), last revised 16 Feb 2026 (this version, v2)] Title:VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery Authors:Gene Yu, Ce Guo, Wayne Luk View a PDF of the paper titled VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery, by Gene Yu and 2 other authors View PDF HTML (experimental) Abstract:Time series causal discovery is essential for understanding dynamic systems, yet many existing methods remain sensitive to noise, non-stationarity, and sampling variability. We propose the Validated Consensus-Driven Framework (VCDF), a simple and method-agnostic layer that improves robustness by evaluating the stability of causal relations across blocked temporal subsets. VCDF requires no modification to base algorithms and can be applied to methods such as VAR-LiNGAM and PCMCI. Experiments on synthetic datasets show that VCDF improves VAR-LiNGAM by approximately 0.08-0.12 in both window and summary F1 scores across diverse data characteristics, with gains most pronounced for moderate-to-long sequences. The framework also benefits from longer sequences, yielding up to 0.18 absolute improvement on time series of length 1000 and above. Evaluations on simulated fMRI data and IT-monitoring scenarios further demonstrate enhanced stability and structural accuracy under realistic noise conditions. VCDF provides an effective reliability layer for time seri...
