Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions

ArXi:2508.21742v2 Announce Type: replace Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full causal structure cannot be fully specified, experts often succeed in providing a high-level abstraction of the causal graph, known as a summary causal graph, which captures the main causal relations between different time series while abstracting away micro-level details.