Efficient Causal Discovery for Autoregressive Time Series

Causal structure learning (CSL) in time series refers to the process of identifying and quantifying potentially time-lagged causal relationships among variables in a system. Unlike traditional time series analysis, which often focuses on prediction and correlation, CSL aims to uncover the cause-and-effect relationships that underlie the observed data. CSL is a crucial challenge in numerous fields such as economics, finance, healthcare, and natural science, where understanding the causal mechanisms can lead to more accurate forecasting, targeted interventions, and improved risk management. Causal structure learning poses significant challenges due to the presence of unobserved confounding factors, limited observational data, non-stationarity, and noise. Traditional CSL methods, which primarily focus on contemporaneous data, address some of these issues, but encounter considerable difficulties when extended to time series data.