Abstract / truncated to 115 words (read the full abstract)

Across various fields of engineering and science, there is great interest in studying causal relationships between time series. Distinguishing cause from effect is difficult in practice for many reasons, including limited access to data, unknown functional relationships, and unobserved confounding factors. Due to these challenges, modern causal inference requires methods that can perform robust detection and estimation, quantify uncertainty, and explain how model’s inputs contribute to its predictions. These challenges are further compounded in time series settings, where autocorrelation and temporal patterns can skew inference. This thesis introduces several contributions to the field of causal inference that aim to address these concerns. The first part of the thesis examines approaches to causal discovery and the ... toggle 11 keywords

causality – causal inference – causal discovery – explainable ai – explainable machine learning – manifolds – cross map – causal effect – interactions – gaussian processes – multivariate time series

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