Python code for causal modeling.
Project description
y0
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):
- Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology
- Pacific Northwest National Laboratory
💰 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:
- Uses BumpVersion to switch the version number in the
setup.cfg
andsrc/y0/version.py
to not have the-dev
suffix - Packages the code in both a tar archive and a wheel
- Uploads to PyPI using
twine
. Be sure to have a.pypirc
file configured to avoid the need for manual input at this step - Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
- 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.
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