3. Causal Graph Algorithms in PyWhy

Pywhy-graphs provides data structures and methods for storing causal graphs, which are documented in Causal Graphs in PyWhy. We also provide a submodule for common graph algorithms in the form of functions that take a mixed-edge graph as input.

3.1. Core Algorithms

is_valid_mec_graph(G[, on_error])	Check G is a valid PAG.
possible_ancestors(G, source)	Possible ancestors of a source node.
possible_descendants(G, source)	Possible descendants of a source node.
discriminating_path(graph, u, a, c[, ...])	Find the discriminating path for <..., a, u, c>.
is_definite_noncollider(G, node1, node2, node3)	Check if <node1, node2, node3> path forms a definite non-collider.
valid_pag(G)	Check if the provided PAG is valid or not.
mag_to_pag(G)	Converts the provided mag into a pag using the FCI algorithm.
pag_to_mag(graph)	Sample a MAG from a PAG using Zhang's algorithm.
check_pag_definition(G[, L, S])	Checks if the provided graph is a valid Partial Ancestral Graph (PAG).

bidirected_to_unobserved_confounder(G[, ...])	Convert all bidirected edges to unobserved confounders.
m_separated(G, x, y, z[, ...])	Check m-separation among 'x' and 'y' given 'z' in mixed-edge causal graph G, which may contain directed, bidirected, and undirected edges.
is_minimal_m_separator(G, x, y, z[, i, r, ...])	Check if a set 'z' is a i-minimal m-separator between 'x' and 'y'.
minimal_m_separator(G, x, y[, i, r, ...])	Find a i-minimal m-separating set 'z' between 'x' and 'y' in mixed-edge causal graph G.

3.2. Algorithms for Markov Equivalence Classes

pds(graph, node_x[, node_y, max_path_length])	Find all PDS sets between node_x and node_y.
pds_path(graph, node_x, node_y[, ...])	Compute the possibly-d-separating set path.
uncovered_pd_path(graph, u, c[, ...])	Compute uncovered potentially directed (pd) paths from u to c.

3.3. Algorithms for Time-Series Graphs

pds_t(graph, node_x, node_y[, max_path_length])	Compute the possibly-d-separating set over time.
pds_t_path(graph, node_x, node_y[, ...])	Compute the possibly-d-separating path set over time.

3.4. Algorithms for handling acyclicity

acyclification(G[, directed_edge_type, ...])

Acyclify a cyclic graph.

4. Semi-directed (possibly-directed) Paths

all_semi_directed_paths(G, source, target[, ...])	Generate all semi-directed paths from source to target in G.
is_semi_directed_path(G, nodes)	Returns True if and only if nodes form a semi-directed path in G.