This package is nearing a V1 release, with potential (minor) breaking changes. After the release, future breaking changes will occur less frequently and with more notice. Please raise issues as needed.
causaldag is common wrapper for the following packages:
- https://github.com/uhlerlab/graphical_models
- https://github.com/uhlerlab/conditional_independence
- https://github.com/uhlerlab/graphical_model_learning
Installing and importing causaldag should be sufficient for most use cases.
CausalDAG is a Python package for the creation, manipulation, and learning of Causal DAGs. CausalDAG requires Python 3.5+
Install the latest version of CausalDAG:
$ pip3 install causaldag
Documentation for each subpackage is available at:
- graphical_models: https://graphical-models.readthedocs.io/en/latest/
- graphical_model_learning: https://graphical-model-learning.readthedocs.io/en/latest/
- conditional_independence: https://conditional-independence.readthedocs.io/en/latest/
Examples for specific algorithms can be found at https://uhlerlab.github.io/causaldag/
Find the CPDAG (complete partially directed acyclic graph, AKA the essential graph) corresponding to a DAG:
>>> from causaldag import rand, partial_correlation_suffstat, partial_correlation_test, MemoizedCI_Tester, gsp
>>> import numpy as np
>>> np.random.seed(12312)
>>> nnodes = 5
>>> nodes = set(range(nnodes))
>>> dag = rand.directed_erdos(nnodes, .5)
>>> gdag = rand.rand_weights(dag)
>>> samples = gdag.sample(100)
>>> suffstat = partial_correlation_suffstat(samples)
>>> ci_tester = MemoizedCI_Tester(partial_correlation_test, suffstat, alpha=1e-3)
>>> est_dag = gsp(nodes, ci_tester)
>>> dag.shd_skeleton(est_dag)
Released under the 3-Clause BSD license (see LICENSE.txt):
Copyright (C) 2018
Chandler Squires <csquires@mit.edu>