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Archive / INF Seminars / INF_2025_03_25_Vittorio Del Tatto

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	A novel framework for causal discovery in high-dimensional time series

Tuesday

25.03

USI Campus EST, Room D0.02

12:30 - 13:30


	Vittorio Del Tatto Scuola Internazionale Superiore di Studi Avanzati

Abstract: Understanding which parts of a dynamical system cause each other is extremely relevant in fundamental and applied sciences. However, inferring causal links from observational time series data, namely without direct manipulations of the system, is still computationally challenging, especially if the data are high-dimensional. In the first part of this seminar, I will introduce a novel approach for inferring the presence of causal relationships from high-dimensional time series data, based on the minimisation of the Information Imbalance measure. In the second part of the seminar, I will extend the proposed measure to tackle the problem of causal network reconstruction, introducing an algorithm whose computational cost scales linearly with the number of variables. The approach is based on the automatic identification of dynamical communities, groups of variables which mutually influence each other and can therefore be described as a single node in a causal graph. These communities are naturally ordered starting from the fully autonomous ones, whose evolution is independent from all the others, to those that are progressively dependent on other communities. This framework provides an efficient and promising alternative for analyzing high-dimensional systems where all-variable time series graphs become unmanageable.

Biography: Vittorio Del Tatto is a fourth-year PhD student at SISSA (Trieste, Italy) in the Physics and Chemistry of Biological Systems program. His research is focused on the development of model-free methodologies for high-dimensional inference and their application to causal discovery for time series and feature selection. He works under the supervision of Prof. Alessandro Laio.

Host: Prof. Dr. Mira Antonietta