causal_networkx.discovery.PC#

class causal_networkx.discovery.PC(ci_estimator, alpha=0.05, init_graph=None, fixed_edges=None, min_cond_set_size=None, max_cond_set_size=None, max_iter=1000, max_combinations=None, apply_orientations=True, **ci_estimator_kwargs)[source]#

Methods

`convert_skeleton_graph`(graph)	Convert skeleton graph as undirected networkx Graph to CPDAG.
`fit`(X)	Fit constraint-based discovery algorithm on dataset 'X'.
`learn_skeleton`(X[, graph, sep_set, fixed_edges])	Learns the skeleton of a causal DAG using pairwise independence testing.
`orient_edges`(skel_graph, sep_set)	Orient edges in a skeleton graph to estimate the causal DAG, or CPDAG.
`test_edge`(data, X, Y[, Z])	Test any specific edge for X \|\| Y \| Z.

convert_skeleton_graph(graph)[source]#

Convert skeleton graph as undirected networkx Graph to CPDAG.

Parameters:

graph : nx.Graph

Converts a skeleton graph to the representation needed for PC algorithm, a CPDAG.

Returns:

graph : CPDAG

The CPDAG class.

:rtype:py:class:CPDAG

fit(X)#

Fit constraint-based discovery algorithm on dataset ‘X’.

Parameters:: X : Union[pd.DataFrame, Dict[Set, pd.DataFrame]]

Either a pandas dataframe constituting the endogenous (observed) variables as columns and samples as rows, or a dictionary of different sampled distributions with keys as the distribution names and values as the dataset as a pandas dataframe.
Raises:: RuntimeError

If ‘X’ is a dictionary, then all datasets should have the same set of column names (nodes).

Notes

Control over the constraints imposed by the algorithm can be passed into the class constructor.

Return type:: None

learn_skeleton(X, graph=None, sep_set=None, fixed_edges=None)#

Learns the skeleton of a causal DAG using pairwise independence testing.

Encodes the skeleton via an undirected graph, networkx.Graph. Only tests with adjacent nodes in the conditioning set.

Parameters:

X : pd.DataFrame

The data with columns as variables and samples as rows.

graph : nx.Graph

The undirected graph containing initialized skeleton of the causal relationships.

sep_set : set

The separating set.

fixed_edges : set, optional

The set of fixed edges. By default, is the empty set.

return_deps : bool

Whether to return the two mappings for the dictionary of test statistic and pvalues.

Returns:

skel_graph : nx.Graph

The undirected graph of the causal graph’s skeleton.

sep_set : dict of dict of set

The separating set per pairs of variables.

Raises:

ValueError

If the nodes in the initialization graph do not match the variable names in passed in data, X.

ValueError

If the nodes in the fixed-edge graph do not match the variable names in passed in data, X.

Notes

Learning the skeleton of a causal DAG uses (conditional) independence testing to determine which variables are (in)dependent. This specific algorithm compares exhaustively pairs of adjacent variables.