Inferring extended summary causal graphs from observational time series
This work addresses causal discovery in time series for researchers, but it is incremental as it builds on existing constraint-based frameworks.
The study tackled the problem of learning extended summary causal graphs from observational time series by introducing generalizations of causation entropy and adapting PC and FCI algorithms, with results illustrated through experiments on simulated and real datasets.
This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two well-known algorithms, namely PC and FCI. The behavior of our methods is illustrated through several experiments run on simulated and real datasets.