HierarchicalStructuralCausalModel

class HierarchicalStructuralCausalModel[source]

A subclass of HCM that wraps HSCM functionality.

Initialize the HSCM.

Methods Summary

`add_edge`(u, v, **kwargs)	Add an edge.
`add_observed_node`(node)	Add an observed node and its exogenous noise.
`add_unobserved_node`(node)	Add an unobserved node and its exogenous noise.
`get_exogenous_noise`()	Return the set of exogenous noise variables in the HSCM.
`to_admg`(*[, return_hcgm])	Return a collapsed hierarchical causal model.
`to_hcgm`()	Convert an HSCM to a hierarchical causal graphical model (HCGM) with promoted Q variables.
`to_hcm`()	Convert the HSCM to a hierarchical causal model (HCM).
`to_pygraphviz`()	Get a pygraphviz object.

Methods Documentation

add_edge(u: str | Variable | QVariable, v: str | Variable | QVariable, **kwargs: Any) → None[source]: Add an edge.

add_observed_node(node: str | Variable | QVariable) → None[source]: Add an observed node and its exogenous noise.

add_unobserved_node(node: str | Variable | QVariable) → None[source]: Add an unobserved node and its exogenous noise.

get_exogenous_noise() → set[Variable][source]: Return the set of exogenous noise variables in the HSCM.

to_admg(*, return_hcgm: bool = False) → NxMixedGraph[source]

Return a collapsed hierarchical causal model.

Parameters:: return_hcgm – if True, returns the intermediate hierarchical causal graphical models (HCGM) with subunits and promoted Q variables
Returns:: a mixed graph

to_hcgm() → HierarchicalCausalModel[source]: Convert an HSCM to a hierarchical causal graphical model (HCGM) with promoted Q variables.

to_hcm() → HierarchicalCausalModel[source]: Convert the HSCM to a hierarchical causal model (HCM).