András Telcs, Marcell T. Kurbucz, Antal Jakovác
arXiv 25 Oct 2024 · Statistics — Methodology · publishedPhysical review. E (2025) · 1 citations (OpenAlex)
arXiv:2410.19469 · PDF · DOI · OpenAlex · Extracted main text
Temporally evolving systems are typically modeled by dynamic equations. A key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems, whether deterministic or stochastic. Notably, the method also uncovers hidden common causes beyond the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zsigmond Benko, Adám Zlatniczki, Marcell Stippinger, Dániel Fabó, An… (2018) Complete inference of causal relations between dynamical systems self | 1.000 | 5 | 3 | 100% |
| 2 | Daniel Malinsky and Peter Spirtes (2018) Causal structure learning from multivariate time series in settings with unmeasured confounding | 0.737 | 3 | 2 | 100% |
| 3 | Walter N Thurman, Mark E Fisher, et al (1988) Chickens, eggs, and causality, or which came first | 0.737 | 3 | 2 | 100% |
| 4 | Anna Krakovská (2019) Correlation dimension detects causal links in coupled dynamical systems | 0.737 | 3 | 2 | 100% |
| 5 | Marcell Stippinger, Attila Bencze, Ádám Zlatniczki, Zoltán Somogyvár… (2023) Causal discovery of stochastic dynamical systems: A markov chain approach self | 0.737 | 3 | 2 | 100% |
| 6 | George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Mi… (2012) Detecting causality in complex ecosystems | 0.737 | 3 | 2 | 100% |
| 7 | Zsigmond Benko, Ádám Zlatniczki, Marcell Stippinger, Dániel Fabó, An… (2024) Bayesian inference of causal relations between dynamical systems self | 0.644 | 2 | 2 | 100% |
| 8 | Wiebke Günther, Urmi Ninad, and Jakob Runge (2023) Causal discovery for time series from multiple datasets with latent contexts | 0.644 | 2 | 2 | 100% |
| 9 | Yoshito Hirata and Kazuyuki Aihara (2010) Identifying hidden common causes from bivariate time series: A method using recurrence plots | 0.644 | 2 | 2 | 100% |
| 10 | Ádám Zlatniczki, Marcell Stippinger, Zsigmond Benko, Zoltán Somogyvá… (2021) Relaxation of some confusions about confounders self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.