# Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations

arXiv: https://arxiv.org/abs/2110.15595 · HTML: https://mlsanity.com/paper/2110.15595/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Group invariance principles for causal generative models](https://mlsanity.com/paper/1705.02212/) (2017)
- [A theory of independent mechanisms for extrapolation in generative models](https://mlsanity.com/paper/2004.00184/) (2020)
- [Telling cause from effect in deterministic linear dynamical systems](https://mlsanity.com/paper/1503.01299/) (2015)

## Built on by (most cited first, 3 of 3)

- [Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks](https://mlsanity.com/paper/2504.07099/) (2025)
- [Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach](https://mlsanity.com/paper/2309.02571/) (2023)
- [GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network](https://mlsanity.com/paper/2302.10804/) (2023)

## Abstract (arXiv)

Distinguishing between cause and effect using time series observational data is a major challenge in many scientific fields. A new perspective has been provided based on the principle of Independence of Causal Mechanisms (ICM), leading to the Spectral Independence Criterion (SIC), postulating that the power spectral density (PSD) of the cause time series is uncorrelated with the squared modulus of the frequency response of the filter generating the effect. Since SIC rests on methods and assumptions in stark contrast with most causal discovery methods for time series, it raises questions regarding what theoretical grounds justify its use. In this paper, we provide answers covering several key aspects. After providing an information theoretic interpretation of SIC, we present an identifiability result that sheds light on the context for which this approach is expected to perform well. We further demonstrate the robustness of SIC to downsampling - an obstacle that can spoil Granger-based inference. Finally, an invariance perspective allows to explore the limitations of the spectral independence assumption and how to generalize it. Overall, these results support the postulate of Spectral Independence is a well grounded leading principle for causal inference based on empirical time series.