# Unified Causality Analysis Based on the Degrees of Freedom

arXiv: https://arxiv.org/abs/2410.19469 · HTML: https://mlsanity.com/paper/2410.19469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2407.13278/) (2024)
- [High-recall causal discovery for autocorrelated time series with latent confounders](https://mlsanity.com/paper/2007.01884/) (2020)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [A Survey on Causal Discovery: Theory and Practice](https://mlsanity.com/paper/2305.10032/) (2023)
- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [Large Language Models and Causal Inference in Collaboration: A Survey](https://mlsanity.com/paper/2403.09606/) (2024)
- [Large Language Models for Causal Discovery: Current Landscape and Future Directions](https://mlsanity.com/paper/2402.11068/) (2024)
- [Causal discovery for time series from multiple datasets with latent contexts](https://mlsanity.com/paper/2306.12896/) (2023)
- [Bootstrap aggregation and confidence measures to improve time series causal discovery](https://mlsanity.com/paper/2306.08946/) (2023)
- [Complete Inference of Causal Relations between Dynamical Systems](https://mlsanity.com/paper/1808.10806/) (2018)
- [On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption](https://mlsanity.com/paper/1802.07051/) (2018)
- [Characterization of causal ancestral graphs for time series with latent confounders](https://mlsanity.com/paper/2112.08417/) (2021)
- [Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods](https://mlsanity.com/paper/2404.05746/) (2024)

## Built on by (most cited first, 1 of 1)

- [ALT: A Python Package for Lightweight Feature Representation in Time Series Classification](https://mlsanity.com/paper/2504.12841/) (2025)

## Abstract (arXiv)

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.