# PQP
The name `pqp` is short for *Pourquoi pas?*. This phrase is French for *why not?*, because "Why not?" was the question we asked ourselves when we found there was no maintained, open-source package for structural causal modeling in Python. The package provides a convenient interface for causal modeling along with routines for identification, estimation, and visualization.
## Installation
The package can be installed from PyPi using `pip`:
```bash
pip install pqp
```
## Basic Usage
```python
from pqp.graph import Graph
from pqp.variable import make_vars
# create variables
x, y, z = make_vars("xyz")
# the backdoor model
g = Graph([
x <= z,
y <= z,
y <= x,
])
# identification
causal_estimand = ATE(y, {x: 1}, {x: 0})
estimator = g.identify(causal_estimand)
print(estimator)
# >>> E_(y) [ Σ_(z) [ [P(x = 1, y, z) * P(z) / P(x = 1, z)] ] ] - E_(y) [ Σ_(z) [ [P(x = 0, y, z) * P(z) / P(x = 0, z)] ] ]
```
## Further Reading
For more information, see the documentation at [https://leo-ware.github.io/pqp/](https://leo-ware.github.io/pqp/).
The source code is available at [https://github.com/leo-ware/pqp](https://github.com/leo-ware/pqp).
## About
This package was created by Leo Ware as part of his undergraduate capstone project at Minerva University.
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"description": "# PQP\n\nThe name `pqp` is short for *Pourquoi pas?*. This phrase is French for *why not?*, because \"Why not?\" was the question we asked ourselves when we found there was no maintained, open-source package for structural causal modeling in Python. The package provides a convenient interface for causal modeling along with routines for identification, estimation, and visualization.\n\n## Installation\n\nThe package can be installed from PyPi using `pip`:\n\n```bash\npip install pqp\n```\n\n## Basic Usage\n\n```python\n\nfrom pqp.graph import Graph\nfrom pqp.variable import make_vars\n\n# create variables\nx, y, z = make_vars(\"xyz\")\n\n# the backdoor model\ng = Graph([\n x <= z,\n y <= z,\n y <= x,\n])\n\n# identification\ncausal_estimand = ATE(y, {x: 1}, {x: 0})\nestimator = g.identify(causal_estimand)\nprint(estimator)\n\n# >>> E_(y) [ \u03a3_(z) [ [P(x = 1, y, z) * P(z) / P(x = 1, z)] ] ] - E_(y) [ \u03a3_(z) [ [P(x = 0, y, z) * P(z) / P(x = 0, z)] ] ]\n\n```\n\n## Further Reading\n\nFor more information, see the documentation at [https://leo-ware.github.io/pqp/](https://leo-ware.github.io/pqp/).\n\nThe source code is available at [https://github.com/leo-ware/pqp](https://github.com/leo-ware/pqp).\n\n## About\n\nThis package was created by Leo Ware as part of his undergraduate capstone project at Minerva University.\n",
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