https://github.com/uhlerlab/causaldag

Python package for the creation, manipulation, and learning of Causal DAGs
https://github.com/uhlerlab/causaldag

causal-dags causal-inference causal-models causality inference

Last synced: 16 days ago
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Python package for the creation, manipulation, and learning of Causal DAGs

• Host: GitHub
• URL: https://github.com/uhlerlab/causaldag
• Owner: uhlerlab
• License: other
• Created: 2018-07-28T21:27:11.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-04-12T05:29:30.000Z (over 1 year ago)
• Last Synced: 2024-08-01T00:48:12.215Z (3 months ago)
• Topics: causal-dags, causal-inference, causal-models, causality, inference
• Language: JavaScript
• Size: 8.77 MB
• Stars: 139
• Watchers: 8
• Forks: 17
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
[![PyPI version](https://badge.fury.io/py/causaldag.svg)](https://badge.fury.io/py/causaldag)

**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

Install the latest version of CausalDAG:

```

$ pip3 install causaldag

```

### Cite

You may use the following bibtex for citing `causaldag`:

```

@manual{squires2018causaldag,

  title={{\texttt{causaldag}: creation, manipulation, and learning of causal models}},

  author={{Chandler Squires}},

  year={2018},

  url={https://github.com/uhlerlab/causaldag},

}

```

Or the following text:

> Chandler Squires. _causaldag: creation, manipulation, and learning of causal models_, 2018. URL https://github.com/uhlerlab/causaldag

### Documentation

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/

### Simple Example

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)

```

### License

Released under the 3-Clause BSD license (see LICENSE.txt):

```

Copyright (C) 2018

Chandler Squires 

```