Quantised causal inference based on directed information
Project Leader: Girish Nair
Staff: Erik Weyer
Student: Salman Ahmadi
Sponsors: ARC Future Fellowship
Primary Contact: Girish Nair (email@example.com)
Keywords: complex systems; knowledge discovery; machine learning; signal processing; system identification
Disciplines: Electrical & Electronic Engineering
Domains: Networks and data in society, Optimisation of resources and infrastructure
In the field of causal inference, the aim is to determine the causality relationships between two or more observed time series, under suitable assumptions on the underlying generating model classes. Causality here is defined in terms of conditional probabilities, based on Granger’s ideas [eg, Amblard and Michel, Entropy, 2013].
All current techniques for causal inference are based on full-resolution measurements of the time series of interest. However, in many remote sensing applications only quantised, low-resolution measurements may be available.
This introduces several major theoretical challenges not addressed in the existing literature.Firstly, existing causality inference/system identification techniques cannot be applied directly. Secondly, optimal quantiser design becomes a critical issue. This project will explore these challenges, by using the Marko-Massey directed information and/or the nonstochastic directed information [Nair, IEEE CDC 2012; Nair, IEEE CDC 2015] as measures of causality.