Fig. 6: Overview of the PCMCI+ causal discovery graph algorithm.
From: Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain
Overview of the PCMCI+ causal discovery graph algorithm. The variables \({x}_{k}\) \((k=1,\,\ldots ,\,4)\) are depicted by nodes and causal interactions are indicated by directed edges. It includes two main steps (PC algorithm) and MCI tests. PC1 starts with a fully connected graph as shown in (a). It then tests for the elimination of links between variables iteratively by conditioning sets of increasing cardinalities as shown in (b). MCI tests use the estimated conditions found in step one to infer a causal link—See (c). The node colors indicate the level of auto-dependency (auto-MCI) of each component and link colors indicate the interdependency strength (cross-MCI) between variables.
