CausalDisco

Name	CausalDisco JSON
Version	0.2.0 JSON
	download
home_page
Summary	Baseline algorithms and analytics tools for Causal Discovery.
upload_time	2023-07-19 14:57:10
maintainer
docs_url	None
author	Alexander G. Reisach, Sebastian Weichwald
requires_python
license
keywords	causality causal discovery
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            [![Latest version](https://badge.fury.io/py/CausalDisco.svg)](https://badge.fury.io/py/CausalDisco)
[![License: BSD](https://img.shields.io/badge/License-BSD-blue.svg)](https://github.com/CausalDisco/CausalDisco/blob/main/LICENSE)
[![Downloads](https://static.pepy.tech/personalized-badge/CausalDisco?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/CausalDisco)


# CausalDisco 🪩

CausalDisco contains baseline algorithms and analytics tools for Causal Discovery. The [package](https://pypi.org/project/CausalDisco/) can be installed by running `pip install CausalDisco`. Additional information can be found in the [documentation](https://causaldisco.github.io/CausalDisco/).

### Baseline Algorithms
Find the following baseline algorithms in __CausalDisco/baselines.py__
- R²-SortnRegress
- Var-SortnRegress
- Random-SortnRegress

### Analytics Tools
Find the following analytics tools in __CausalDisco/analytics.py__
- R²-sortability
- Var-sortability
- order_alignment

### Sources
If you find our algorithms useful please consider citing
- [Beware of the Simulated DAG!](https://proceedings.neurips.cc/paper_files/paper/2021/file/e987eff4a7c7b7e580d659feb6f60c1a-Supplemental.pdf)
- [Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models](https://arxiv.org/abs/2303.18211).
```
@article{reisach2021beware,
  title={Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game},
  author={Reisach, Alexander G. and Seiler, Christof and Weichwald, Sebastian},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@article{reisach2023simple,
  title={Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models},
  author={Reisach, Alexander G. and Tami, Myriam and Seiler, Christof and Chambaz, Antoine and Weichwald, Sebastian},
  journal={arXiv preprint arXiv:2303.18211},
  year={2023}
}
```

### A Simple Example
```python
# --- sample data from a linear SCM:

import numpy as np
from scipy import linalg
from CausalDisco.analytics import var_sortability, r2_sortability

d = 10
W = np.diag(np.ones(d-1), 1)
X = np.random.randn(10000, d).dot(linalg.inv(np.eye(d) - W))
X_std = (X - np.mean(X, axis=0))/np.std(X, axis=0)

# --- run analytics and print results:

from CausalDisco.analytics import (
    var_sortability,
    r2_sortability,
    snr_sortability
)

print(
    f'True\n{W}\n'
    f'var-sortability={var_sortability(X, W):.2f}\n'
    f'R^2-sortability={r2_sortability(X, W):.2f}\n'
    f'SNR-sortability={snr_sortability(X, W):.2f}'
)

# --- run baselines and print results:

from CausalDisco.baselines import (
    random_sort_regress,
    var_sort_regress,
    r2_sort_regress
)

print(
    f'True\n{W}\n'
    '--- randomRegress ---\n'
    f'Recovered:\n{1.0*(random_sort_regress(X)!=0)}\n'
    f'Recovered standardized:\n{1.0*(random_sort_regress(X_std)!=0)}\n'
    '--- varSortnRegress ---\n'
    f'Recovered:\n{1.0*(var_sort_regress(X)!=0)}\n'
    f'Recovered standardized:\n{1.0*(var_sort_regress(X_std)!=0)}\n'
    '--- r2SortnRegress ---\n'
    f'Recovered:\n{1.0*(r2_sort_regress(X)!=0)}\n'
    f'Recovered standardized:\n{1.0*(r2_sort_regress(X_std)!=0)}\n'
)
```


            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "CausalDisco",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "causality,causal discovery",
    "author": "Alexander G. Reisach, Sebastian Weichwald",
    "author_email": "",
    "download_url": "https://files.pythonhosted.org/packages/5c/4f/0b926c46d22f4348b65b38f28c163bf3f924e850295a36c0a0fda8f64ef4/causaldisco-0.2.0.tar.gz",
    "platform": null,
    "description": "[![Latest version](https://badge.fury.io/py/CausalDisco.svg)](https://badge.fury.io/py/CausalDisco)\n[![License: BSD](https://img.shields.io/badge/License-BSD-blue.svg)](https://github.com/CausalDisco/CausalDisco/blob/main/LICENSE)\n[![Downloads](https://static.pepy.tech/personalized-badge/CausalDisco?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/CausalDisco)\n\n\n# CausalDisco \ud83e\udea9\n\nCausalDisco contains baseline algorithms and analytics tools for Causal Discovery. The [package](https://pypi.org/project/CausalDisco/) can be installed by running `pip install CausalDisco`. Additional information can be found in the [documentation](https://causaldisco.github.io/CausalDisco/).\n\n### Baseline Algorithms\nFind the following baseline algorithms in __CausalDisco/baselines.py__\n- R\u00b2-SortnRegress\n- Var-SortnRegress\n- Random-SortnRegress\n\n### Analytics Tools\nFind the following analytics tools in __CausalDisco/analytics.py__\n- R\u00b2-sortability\n- Var-sortability\n- order_alignment\n\n### Sources\nIf you find our algorithms useful please consider citing\n- [Beware of the Simulated DAG!](https://proceedings.neurips.cc/paper_files/paper/2021/file/e987eff4a7c7b7e580d659feb6f60c1a-Supplemental.pdf)\n- [Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models](https://arxiv.org/abs/2303.18211).\n```\n@article{reisach2021beware,\n  title={Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game},\n  author={Reisach, Alexander G. and Seiler, Christof and Weichwald, Sebastian},\n  journal={Advances in Neural Information Processing Systems},\n  volume={34},\n  year={2021}\n}\n\n@article{reisach2023simple,\n  title={Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models},\n  author={Reisach, Alexander G. and Tami, Myriam and Seiler, Christof and Chambaz, Antoine and Weichwald, Sebastian},\n  journal={arXiv preprint arXiv:2303.18211},\n  year={2023}\n}\n```\n\n### A Simple Example\n```python\n# --- sample data from a linear SCM:\n\nimport numpy as np\nfrom scipy import linalg\nfrom CausalDisco.analytics import var_sortability, r2_sortability\n\nd = 10\nW = np.diag(np.ones(d-1), 1)\nX = np.random.randn(10000, d).dot(linalg.inv(np.eye(d) - W))\nX_std = (X - np.mean(X, axis=0))/np.std(X, axis=0)\n\n# --- run analytics and print results:\n\nfrom CausalDisco.analytics import (\n    var_sortability,\n    r2_sortability,\n    snr_sortability\n)\n\nprint(\n    f'True\\n{W}\\n'\n    f'var-sortability={var_sortability(X, W):.2f}\\n'\n    f'R^2-sortability={r2_sortability(X, W):.2f}\\n'\n    f'SNR-sortability={snr_sortability(X, W):.2f}'\n)\n\n# --- run baselines and print results:\n\nfrom CausalDisco.baselines import (\n    random_sort_regress,\n    var_sort_regress,\n    r2_sort_regress\n)\n\nprint(\n    f'True\\n{W}\\n'\n    '--- randomRegress ---\\n'\n    f'Recovered:\\n{1.0*(random_sort_regress(X)!=0)}\\n'\n    f'Recovered standardized:\\n{1.0*(random_sort_regress(X_std)!=0)}\\n'\n    '--- varSortnRegress ---\\n'\n    f'Recovered:\\n{1.0*(var_sort_regress(X)!=0)}\\n'\n    f'Recovered standardized:\\n{1.0*(var_sort_regress(X_std)!=0)}\\n'\n    '--- r2SortnRegress ---\\n'\n    f'Recovered:\\n{1.0*(r2_sort_regress(X)!=0)}\\n'\n    f'Recovered standardized:\\n{1.0*(r2_sort_regress(X_std)!=0)}\\n'\n)\n```\n\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "Baseline algorithms and analytics tools for Causal Discovery.",
    "version": "0.2.0",
    "project_urls": null,
    "split_keywords": [
        "causality",
        "causal discovery"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "f165de0c5a284472ad2bfccf3827fb09cd16f9d2c5c700589daa44bf41f72687",
                "md5": "db41f693a3c8f41e05f7071dc58ebb5c",
                "sha256": "95015817a806595279788645ec37bba5b4e42aace7b9fa9949bace2369c46a43"
            },
            "downloads": -1,
            "filename": "causaldisco-0.2.0-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "db41f693a3c8f41e05f7071dc58ebb5c",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 5569,
            "upload_time": "2023-07-19T14:57:09",
            "upload_time_iso_8601": "2023-07-19T14:57:09.445515Z",
            "url": "https://files.pythonhosted.org/packages/f1/65/de0c5a284472ad2bfccf3827fb09cd16f9d2c5c700589daa44bf41f72687/causaldisco-0.2.0-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5c4f0b926c46d22f4348b65b38f28c163bf3f924e850295a36c0a0fda8f64ef4",
                "md5": "e97460588b6063c0b51dfd9cd90c3213",
                "sha256": "e1a120b99e4f773f73f26bf2f0de7e840f3224667f82b4e8698aea09c95ae0da"
            },
            "downloads": -1,
            "filename": "causaldisco-0.2.0.tar.gz",
            "has_sig": false,
            "md5_digest": "e97460588b6063c0b51dfd9cd90c3213",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 4110,
            "upload_time": "2023-07-19T14:57:10",
            "upload_time_iso_8601": "2023-07-19T14:57:10.872217Z",
            "url": "https://files.pythonhosted.org/packages/5c/4f/0b926c46d22f4348b65b38f28c163bf3f924e850295a36c0a0fda8f64ef4/causaldisco-0.2.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-07-19 14:57:10",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "causaldisco"
}