2022-06-28-besserve22a.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
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) for time series causally unidirectionally linked by a linear time-invariant relation. SIC postulates that the power spectral density (PSD) of the cause time series is {\it 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 provide insights on how the ICM principle can be assessed mathematically to infer direction of causation in empirical time series.	First Conference on Causal Learning and Reasoning	Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	besserve22a	0	Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations	110	143	110-143	110	false	Besserve, Michel and Shajarisales, Naji and Janzing, Dominik and Sch{\"o}lkopf, Bernhard	given family Michel Besserve given family Naji Shajarisales given family Dominik Janzing given family Bernhard Schölkopf	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/besserve22a/besserve22a.pdf