Unified Causality Analysis Based on the Degrees of Freedom

András Telcs, Marcell T. Kurbucz, Antal Jakovác
10/25/2024
stat.MEcs.AIcs.LG

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Review on Causality Detection Based on Empirical Dynamic Modeling

81% match2023

Building causation links in stochastic nonlinear systems from data

80% match2025

Re-examining Granger Causality from Causal Bayesian Networks Perspective

80% match2025

Assimilative Causal Inference

80% match2025

Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders

80% match1 repo52024

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

80% match2024

Cite this paper

@article{telcs2024unified,
  title  = {Unified Causality Analysis Based on the Degrees of Freedom},
  author = {András Telcs and Marcell T. Kurbucz and Antal Jakovác},
  year   = {2024},
  eprint = {2410.19469},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.19469v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2410.19469.svg)](https://codeofpaper.com/paper/2410.19469)

Discussion