pywhy_graphs.AugmentedGraph#
- class pywhy_graphs.AugmentedGraph(incoming_directed_edges=None, incoming_bidirected_edges=None, incoming_undirected_edges=None, directed_edge_name: str = 'directed', bidirected_edge_name: str = 'bidirected', undirected_edge_name: str = 'undirected', **attr)[source]#
An augmented causal diagram.
An augmented graph is one where interventions are represented by F-nodes. See [1], where they were first introduced. They allow one to model hard and soft interventions as an explicit “F-node” added to the existing causal graph. For more information, see <TBD user guide>.
- Parameters:
- incoming_directed_edgesinput directed edges (optional, default: None)
Data to initialize directed edges. All arguments that are accepted by
networkx.DiGraphare accepted.- incoming_bidirected_edgesinput bidirected edges (optional, default: None)
Data to initialize bidirected edges. All arguments that are accepted by
networkx.Graphare accepted.- incoming_undirected_edgesinput undirected edges (optional, default: None)
Data to initialize undirected edges. All arguments that are accepted by
networkx.Graphare accepted.- directed_edge_namestr
The name for the directed edges. By default ‘directed’.
- bidirected_edge_namestr
The name for the bidirected edges. By default ‘bidirected’.
- undirected_edge_namestr
The name for the directed edges. By default ‘undirected’.
- attrkeyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
Notes
Edge Type Subgraphs
Different edge types in an I-PAG are represented exactly as they are in a
pywhy_graphs.PAG.F-nodes
F-nodes are represented in pywhy-graphs as a tuple as
('F', <index>), whereindexis just a random index number. Each F-node is mapped to the intervention-set that they are applied on. For example in the graph \(('F', 0) \rightarrow X \rightarrow Y\),('F', 0)is the F-node added that models an intervention onX. Each intervention-set is a set of regular nodes in the causal graph.References