Skip to content

Latest commit

 

History

History
118 lines (89 loc) · 2.98 KB

api.rst

File metadata and controls

118 lines (89 loc) · 2.98 KB

API

:py:mod:`pywhy_graphs`:

.. automodule:: pywhy_graphs
   :no-members:
   :no-inherited-members:

This is the application programming interface (API) reference for classes (CamelCase names) and functions (underscore_case names) of pywhy-graphs, grouped thematically by analysis stage.

Causal graph classes

These are the causal classes for Structural Causal Models (SCMs), or various causal graphs encountered in the literature.

.. currentmodule:: pywhy_graphs


.. autosummary::
   :toctree: generated/

   ADMG
   CPDAG
   PAG

Algorithms for Markov Equivalence Classes

Traditional graph algorithms operate over graphs with only one type of edge. Equivalence class graphs in causality generally consist of more than one type of edge. These algorithms are common algorithms used in a variety of different causal graph operations.

.. currentmodule:: pywhy_graphs.algorithms


.. autosummary::
   :toctree: generated/

   is_valid_mec_graph
   possible_ancestors
   possible_descendants
   discriminating_path
   pds
   pds_path
   uncovered_pd_path
   acyclification

Conversions between other package's causal graphs

Other packages, such as causal-learn, implement various causal inference procedures, but encode a causal graph object differently. This submodule converts between those causal graph data structures and corresponding causal graphs in pywhy-graphs.

.. currentmodule:: pywhy_graphs.array


.. autosummary::
   :toctree: generated/

   graph_to_arr
   clearn_arr_to_graph

NetworkX Experimental Functionality

Currently, NetworkX does not support mixed-edge graphs, which are crucial for representing causality with latent confounders and selection bias. The following represent functionality that we intend to PR eventually into networkx. They are included in pywhy-graphs as a temporary bridge. We welcome feedback.

.. currentmodule:: pywhy_graphs.networkx

.. autosummary::
   :toctree: generated/

   MixedEdgeGraph
   bidirected_to_unobserved_confounder
   m_separated

Timeseries

The following are useful functions that operate specifically on time-series graphs.

.. currentmodule:: pywhy_graphs.classes.timeseries

.. autosummary::
   :toctree: generated/

   complete_ts_graph
   empty_ts_graph
   get_summary_graph
   has_homologous_edges
   nodes_in_time_order

Visualization of causal graphs

Visualization of causal graphs is different compared to networkx because causal graphs can consist of mixed-edges. We implement an API that wraps graphviz and pygraphviz to perform modular visualization of nodes and edges.

.. currentmodule:: pywhy_graphs.viz


.. autosummary::
   :toctree: generated/

   draw
   timeseries_layout

Utilities for debugging

.. currentmodule:: pywhy_graphs


.. autosummary::
   :toctree: generated/

   sys_info