PyWhy-Graphs

pywhy-graphs is a Python graph library that extends networkx with the notion of a MixedEdgeGraph to implement a light-weight API for causal graphical structures that contain mixed-edges and contain causal graph traversal algorithms.

Note: The API is subject to change without deprecation cycles due to the current work-in-progress MixedEdgeGraph class in networkx. For more information, follow the PR at https://github.com/networkx/networkx/pull/5947

Why?

Representation of causal graphical models in Python are severely lacking.

PyWhy-Graphs implements a graphical API layer for ADMG, CPDAG and PAG. For causal DAGs, we recommend using the networkx.DiGraph class and ensuring acylicity via networkx.is_directed_acyclic_graph function.

Existing packages that aim to represent causal graphs either break from the networkX API, or only implement a subset of the relevant causal graphs. By keeping in-line with the robust NetworkX API, we aim to ensure a consistent user experience and a gentle introduction to causal graphical models. A MixedEdgeGraph instance is a composition of networkx graphs and has a similar API, with the additional notion of an "edge type", which specifies what edge type subgraph any function should operate over. For example:

# adds a directed edge from x to y
G.add_edge('x', 'y', edge_type='directed')

# adds a bidirected edge from x to y
G.add_edge('x', 'y', edge_type='bidirected')

Moreover, sampling from causal models is non-trivial, but a requirement for benchmarking many causal algorithms in discovery, ID, estimation and more. We aim to provide simulation modules that are easily connected with causal graphs to provide a simple robust API for modeling causal graphs and then simulating data.

Documentation

See the development version documentation.

Or see stable version documentation

Installation

Installation is best done via pip or conda. For developers, they can also install from source using pip. See installation page for full details.

Dependencies

We aim to provide a very light-weight dependency structure. Minimally, pywhy-graphs requires:

* Python (>=3.8)
* numpy
* scipy
* networkx

Additional functionality may be required when running unit-tests and documentation.

User Installation

If you already have a working installation of numpy, scipy and networkx, the easiest way to install pywhy-graphs is using pip:

# doesn't work until we make an official release :p
pip install -U pywhy-graphs

To install the package from github, clone the repository and then cd into the directory. You can then use poetry to install:

poetry install

# for vizualizing graph functionality
poetry install --extras viz

# if you would like an editable install of dodiscover for dev purposes
pip install -e .

pip install https://api.github.com/repos/py-why/pywhy-graphs/zipball/main

Contributing

Pywhy-Graphs is always looking for new contributors to help make the package better, whether it is algorithms, documentation, examples of graph usage, and more! Contributing to Pywhy-Graphs will be rewarding because you will contribute to a much needed package for causal inference.

See our contributing guide for more details.