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

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

It can also be used to manipulate expressions:

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

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

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

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 Star Shpitser and Pearl, 2012 IDC Star Shpitser and Pearl, 2012 Surrogate Outcomes Tikka and Karvanen, 2018 Counterfactual Transportability Correia, Lee, Bareinboim, 2022 Hierarchical Causal Models Weinstein and Blei, 2024 Cyclic ID Forré and Mooij, 2019

Apply an algorithm to an Acyclic Directed Mixed Graph (ADMG) and a causal query to generate an estimand represented in the DSL like:

The most recent release can be installed from PyPI with uv:

or with pip:

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

or with pip:

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

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

Causal identification with Y0 Hoyt, C.T., et al. (2025) arXiv, 2508.03167

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

• Biopragmatics Lab
• Gyori Lab for Computational Biomedicine
• Harvard Program in Therapeutic Science – Laboratory of Systems Pharmacology
• Pacific Northwest National Laboratory

This project has been supported 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

This package was created with @audreyfeldroy’s cookiecutter package using @cthoyt’s cookiecutter-snekpack template.

See developer instructions

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

To install in development mode, use the following:

Alternatively, install using pip:

You can optionally use pre-commit to automate running key code quality checks on each commit. Enable it with:

Or using pip:

After cloning the repository and installing tox with uv tool install tox -with tox-uv or python3 -m pip install tox tox-uv, the unit tests in the tests/ folder can be run reproducibly with:

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

The documentation can be built locally using the following:

The documentation automatically installs the package as well as the docs extra specified in the pyproject.toml. sphinx plugins like texext can be added there. Additionally, they need to be added to the extensions list in docs/source/conf.py.

The documentation can be deployed to ReadTheDocs using this guide. The .readthedocs.yml YAML file contains all the configuration you’ll need. You can also set up continuous integration on GitHub to check not only that Sphinx can build the documentation in an isolated environment (i.e., with tox -e docs-test) but also that ReadTheDocs can build it too.

See maintainer instructions

ReadTheDocs is an external documentation hosting service that integrates with GitHub’s CI/CD. Do the following for each repository:

1. Log in to ReadTheDocs with your GitHub account to install the integration at https://readthedocs.org/accounts/login/?next=/dashboard/
2. Import your project by navigating to https://readthedocs.org/dashboard/import then clicking the plus icon next to your repository
3. You can rename the repository on the next screen using a more stylized name (i.e., with spaces and capital letters)
4. Click next, and you’re good to go!

Zenodo is a long-term archival system that assigns a DOI to each release of your package. Do the following for each repository:

1. Log in to Zenodo via GitHub with this link: https://zenodo.org/oauth/login/github/?next=%2F. This brings you to a page that lists all of your organizations and asks you to approve installing the Zenodo app on GitHub. Click “grant” next to any organizations you want to enable the integration for, then click the big green “approve” button. This step only needs to be done once.
2. Navigate to https://zenodo.org/account/settings/github/, which lists all of your GitHub repositories (both in your username and any organizations you enabled). Click the on/off toggle for any relevant repositories. When you make a new repository, you’ll have to come back to this

After these steps, you’re ready to go! After you make “release” on GitHub (steps for this are below), you can navigate to https://zenodo.org/account/settings/github/repository/y0-causal-inference/y0 to see the DOI for the release and link to the Zenodo record for it.

The Python Package Index (PyPI) hosts packages so they can be easily installed with pip, uv, and equivalent tools.

1. Register for an account here
2. Navigate to https://pypi.org/manage/account and make sure you have verified your email address. A verification email might not have been sent by default, so you might have to click the “options” dropdown next to your address to get to the “re-send verification email” button
3. 2-Factor authentication is required for PyPI since the end of 2023 (see this blog post from PyPI). This means you have to first issue account recovery codes, then set up 2-factor authentication
4. Issue an API token from https://pypi.org/manage/account/token

This only needs to be done once per developer.

This needs to be done once per machine.

Note that this deprecates previous workflows using .pypirc.

After installing the package in development mode and installing tox with uv tool install tox -with tox-uv or python3 -m pip install tox tox-uv, run the following from the console:

This script does the following:

1. Uses bump-my-version to switch the version number in the pyproject.toml, CITATION.cff, src/y0/version.py, and docs/source/conf.py to not have the -dev suffix
2. Packages the code in both a tar archive and a wheel using uv build
3. Uploads to PyPI using uv publish.
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.

1. Navigate to https://github.com/y0-causal-inference/y0/releases/new to draft a new release
2. Click the “Choose a Tag” dropdown and select the tag corresponding to the release you just made
3. Click the “Generate Release Notes” button to get a quick outline of recent changes. Modify the title and description as you see fit
4. Click the big green “Publish Release” button

This will trigger Zenodo to assign a DOI to your release as well.

This project uses cruft to keep boilerplate (i.e., configuration, contribution guidelines, documentation configuration) up-to-date with the upstream cookiecutter package. Install cruft with either uv tool install cruft or python3 -m pip install cruft then run:

More info on Cruft’s update command is available here.