Skip to main content

Python code for causal modeling.

Project description

y0

Tests Cookiecutter template from @cthoyt PyPI PyPI - Python Version PyPI - License Documentation Status DOI Code style: black

y0 (pronounced "why not?") is Python code for causal inference.

💪 Getting Started

Representing Probability Expressions

y0 has a fully featured internal domain specific language for representing probability expressions:

from y0.dsl import P, A, B

# The probability of A given B
expr_1 = P(A | B)

# The probability of A given not B
expr_2 = P(A | ~B)

# The joint probability of A and B
expr_3 = P(A, B)

It can also be used to manipulate expressions:

from y0.dsl import P, A, B, Sum

P(A, B).marginalize(A) == Sum[A](P(A, B))
P(A, B).conditional(A) == P(A, B) / Sum[A](P(A, B))

DSL objects can be converted into strings with str() and parsed back using y0.parser.parse_y0().

A full demo of the DSL can be found in this Jupyter Notebook

Representing Causality

y0 has a notion of acyclic directed mixed graphs built on top of networkx that can be used to model causality:

from y0.graph import NxMixedGraph
from y0.dsl import X, Y, Z1, Z2

# Example from:
#   J. Pearl and D. Mackenzie (2018)
#   The Book of Why: The New Science of Cause and Effect.
#   Basic Books, p. 240.
napkin = NxMixedGraph.from_edges(
    directed=[
        (Z2, Z1),
        (Z1, X),
        (X, Y),
    ],
    undirected=[
        (Z2, X),
        (Z2, Y),
    ],
)

y0 has many pre-written examples in y0.examples from Pearl, Shpitser, Bareinboim, and others.

do Calculus

y0 provides actual implementations of many algorithms that have remained unimplemented for the last 15 years of publications including:

Algorithm Reference
ID Shpitser and Pearl, 2006
IDC Shpitser and Pearl, 2008
ID* Shpitser and Pearl, 2012
IDC* Shpitser and Pearl, 2012

Apply an algorithm to an ADMG and a causal query to generate an estimand represented in the DSL like:

from y0.dsl import P, X, Y
from y0.examples import napkin
from y0.algorithm.identify import Identification, identify

# TODO after ID* and IDC* are done, we'll update this interface
query = Identification.from_expression(graph=napkin, query=P(Y @ X))
estimand = identify(query)
assert estimand == P(Y @ X)

🚀 Installation

The most recent release can be installed from PyPI with:

$ pip install y0

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/y0-causal-inference/y0.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the BSD-3-Clause license.

📖 Citation

Before we publish an application note on y0, you can cite this software via our Zenodo record (also see the badge above):

@software{y0,
  author       = {Charles Tapley Hoyt and
                  Jeremy Zucker and
                  Marc-Antoine Parent},
  title        = {y0-causal-inference/y0},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.1.0},
  doi          = {10.5281/zenodo.4950768},
  url          = {https://doi.org/10.5281/zenodo.4950768}
}

🙏 Supporters

This project has been supported by several organizations (in alphabetical order):

💰 Funding

The development of the Y0 Causal Inference Engine has been funded by the following grants:

Funding Body Program Grant
DARPA Automating Scientific Knowledge Extraction (ASKE) HR00111990009
PNNL Data Model Convergence Initiative Causal Inference and Machine Learning Methods for Analysis of Security Constrained Unit Commitment (SCY0) 90001
DARPA Automating Scientific Knowledge Extraction and Modeling (ASKEM) HR00112220036

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-python-package template.

🛠️ Development

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution.

Developer Installation

To install in development mode, use the following:

$ git clone git+https://github.com/y0-causal-inference/y0.git
$ cd y0
$ pip install -e .

❓ Testing

After cloning the repository and installing tox with pip install tox, the unit tests in the tests/ folder can be run reproducibly with:

$ tox

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

📦 Making a Release

After installing the package in development mode and installing tox with pip install tox, the commands for making a new release are contained within the finish environment in tox.ini. Run the following from the shell:

$ tox -e finish

This script does the following:

  1. Uses BumpVersion to switch the version number in the setup.cfg and src/y0/version.py to not have the -dev suffix
  2. Packages the code in both a tar archive and a wheel
  3. Uploads to PyPI using twine. Be sure to have a .pypirc file configured to avoid the need for manual input at this step
  4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
  5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion minor after.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

y0-0.2.0.tar.gz (258.2 kB view details)

Uploaded Source

Built Distribution

y0-0.2.0-py3-none-any.whl (108.4 kB view details)

Uploaded Python 3

File details

Details for the file y0-0.2.0.tar.gz.

File metadata

  • Download URL: y0-0.2.0.tar.gz
  • Upload date:
  • Size: 258.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.3

File hashes

Hashes for y0-0.2.0.tar.gz
Algorithm Hash digest
SHA256 25fe2d4e313dcfa527477e25ac85b093e6bb51af86a43f03bc44a506b793165e
MD5 e6c0e9e98f12c1d3f6be978196381f01
BLAKE2b-256 7de1ae24f1e4b78b7e2d9ea10c80a4fda10c75347c14f81af40b90a1f1c3f231

See more details on using hashes here.

Provenance

File details

Details for the file y0-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: y0-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 108.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.3

File hashes

Hashes for y0-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 92eea9c7910bf7da6514a418580ad803d34050f998cccc4f146a9a7b0ca2c237
MD5 a91d25c519e48cd738081528a0961a6d
BLAKE2b-256 62a6f41eb1f1cac9ff54964694fd0c206f3d1e49f10df79c811b5262b9e1b174

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page