A Dynamical View of Causal Reasoning in Multivariate Time Series Data Generated by Stochastic Processes

The author proposes a learning paradigm to establish causation between events in time series data, offering formal and computational tools for uncovering and quantifying causal relationships. The approach reframes causation as a machine learning problem using raw observational data.