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:

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:

🍪 Cookiecutter

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

🛠️ Development

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

$ tox

$ tox -e finish