# CausationEntropy
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://causationentropy.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/kslote1/causationentropy)
[](https://github.com/kslote1/causationentropy/actions)
A Python library for discovering causal networks from time series data using **Optimal Causation Entropy (oCSE)**.
## Overview
CausationEntropy implements state-of-the-art information-theoretic methods for causal discovery from multivariate time series. The library provides robust algorithms that can identify causal relationships while controlling for confounding variables and false discoveries.
### What it does
Given time series data, CausationEntropy finds which variables cause changes in other variables by:
1. **Predictive Testing**: Testing if knowing variable X at time t helps predict variable Y at time t+1
2. **Information Theory**: Using conditional mutual information to measure predictive relationships
3. **Statistical Control**: Rigorous statistical testing to avoid false discoveries
4. **Multiple Methods**: Supporting various information estimators and discovery algorithms
## Installation
### From PyPI (recommended)
```bash
pip install causationentropy
```
### Development Installation
```bash
git clone https://github.com/kslote1/causationentropy.git
cd causationentropy
pip install -e .[dev,docs,plotting]
```
## Quick Start
### Basic Usage
```python
import numpy as np
import pandas as pd
from causationentropy import discover_network
# Load your time series data (variables as columns, time as rows)
data = pd.read_csv('your_data.csv')
# Discover causal network
network = discover_network(data, method='standard', max_lag=5)
# Examine results
print(f"Found {network.number_of_edges()} causal relationships")
for source, sink in network.edges(data=True):
print(f"{source} → {sink}: {network[source][sink]}")
```
### Advanced Configuration
```python
# Configure discovery parameters
network = discover_network(
data,
method='standard', # 'standard', 'alternative', 'information_lasso', or 'lasso'
information='gaussian', # 'gaussian', 'knn', 'kde', 'geometric_knn', or 'poisson'
max_lag=5, # Maximum time lag to consider
alpha_forward=0.05, # Forward selection significance
alpha_backward=0.05, # Backward elimination significance
n_shuffles=200 # Permutation test iterations
)
```
### Synthetic Data Example
```python
from causationentropy.datasets import synthetic
# Generate synthetic causal time series
data, true_network = synthetic.linear_stochastic_gaussian_process(
n_variables=5,
n_samples=1000,
sparsity=0.3
)
# Discover network
discovered = discover_network(data)
# Compare with ground truth
print(f"True edges: {true_network.number_of_edges()}")
print(f"Discovered edges: {discovered.number_of_edges()}")
```
## Key Features
- **Multiple Algorithms**: Standard, alternative, information lasso, and lasso variants of oCSE
- **Flexible Information Estimators**: Gaussian, k-NN, KDE, geometric k-NN, and Poisson methods
- **Statistical Rigor**: Permutation-based significance testing with comprehensive test coverage
- **Synthetic Data**: Built-in generators for testing and validation
- **Visualization**: Network plotting and analysis tools
- **Performance**: Optimized implementations with parallel processing support
## Mathematical Foundation
The algorithm uses **conditional mutual information** to quantify causal relationships:
$$I(X; Y | Z) = H(X | Z) + H(Y | Z) - H(X, Y | Z)$$
This measures how much variable X tells us about variable Y, beyond what we already know from conditioning set Z.
**Causal Discovery Rule**: Variable X causes Y if knowing X(t) significantly improves prediction of Y(t+1), even when controlling for all other relevant variables.
The algorithm implements a two-phase approach:
1. **Forward Selection**: Iteratively adds predictors that maximize conditional mutual information
2. **Backward Elimination**: Removes predictors that lose significance when conditioned on others
## Documentation
📚 **[Read the full documentation on ReadTheDocs](https://causationentropy.readthedocs.io/)**
- **[API Reference](https://causationentropy.readthedocs.io/en/latest/api/)**: Complete function and class documentation
- **[User Guide](https://causationentropy.readthedocs.io/en/latest/user_guide/)**: Detailed tutorials and examples
- **[Theory](https://causationentropy.readthedocs.io/en/latest/theory/)**: Mathematical background and algorithms
- **Examples**: Check the `examples/` and `notebooks/` directories
- **Research Papers**: See the `papers/` directory for theoretical foundations
### Local Documentation
Build documentation locally:
```bash
cd docs/
make html
# Open docs/_build/html/index.html
```
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## Citation
If you use this library in your research, please cite:
```bibtex
@misc{slote2025causationentropy,
author = {Slote, Kevin and Fish Jeremie and Bollt, Erirk},
title = {CausationEntropy: A Python Library for Causal Discovery},
url = {https://github.com/kslote1/causationentropy},
doi = {}
}
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details.
## Support
- **Issues**: [GitHub Issues](https://github.com/kslote1/causationentropy/issues)
- **Discussions**: [GitHub Discussions](https://github.com/kslote1/causationentropy/discussions)
- **Email**: kslote1@gmail.com
## Acknowledgments
This work builds upon fundamental research in information theory, causal inference, and time series analysis.
Special thanks to the open-source scientific Python community.
[Original Code](https://github.com/jefish003/NetworkInference)
## LLM Disclosure
Generative AI was used to help with doc strings, documentation, and unit tests.
Raw data
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"description": "# CausationEntropy\n\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n[](https://causationentropy.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/gh/kslote1/causationentropy)\n[](https://github.com/kslote1/causationentropy/actions)\n\nA Python library for discovering causal networks from time series data using **Optimal Causation Entropy (oCSE)**.\n\n## Overview\n\nCausationEntropy implements state-of-the-art information-theoretic methods for causal discovery from multivariate time series. The library provides robust algorithms that can identify causal relationships while controlling for confounding variables and false discoveries.\n\n### What it does\n\nGiven time series data, CausationEntropy finds which variables cause changes in other variables by:\n\n1. **Predictive Testing**: Testing if knowing variable X at time t helps predict variable Y at time t+1\n2. **Information Theory**: Using conditional mutual information to measure predictive relationships\n3. **Statistical Control**: Rigorous statistical testing to avoid false discoveries\n4. **Multiple Methods**: Supporting various information estimators and discovery algorithms\n\n## Installation\n\n### From PyPI (recommended)\n```bash\npip install causationentropy\n```\n\n### Development Installation\n```bash\ngit clone https://github.com/kslote1/causationentropy.git\ncd causationentropy\npip install -e .[dev,docs,plotting]\n```\n\n## Quick Start\n\n### Basic Usage\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom causationentropy import discover_network\n\n# Load your time series data (variables as columns, time as rows)\ndata = pd.read_csv('your_data.csv')\n\n# Discover causal network\nnetwork = discover_network(data, method='standard', max_lag=5)\n\n# Examine results\nprint(f\"Found {network.number_of_edges()} causal relationships\")\nfor source, sink in network.edges(data=True):\n print(f\"{source} \u2192 {sink}: {network[source][sink]}\")\n```\n\n### Advanced Configuration\n\n```python\n# Configure discovery parameters\nnetwork = discover_network(\n data,\n method='standard', # 'standard', 'alternative', 'information_lasso', or 'lasso'\n information='gaussian', # 'gaussian', 'knn', 'kde', 'geometric_knn', or 'poisson'\n max_lag=5, # Maximum time lag to consider\n alpha_forward=0.05, # Forward selection significance\n alpha_backward=0.05, # Backward elimination significance\n n_shuffles=200 # Permutation test iterations\n)\n```\n\n### Synthetic Data Example\n\n```python\nfrom causationentropy.datasets import synthetic\n\n# Generate synthetic causal time series\ndata, true_network = synthetic.linear_stochastic_gaussian_process(\n n_variables=5, \n n_samples=1000, \n sparsity=0.3\n)\n\n# Discover network\ndiscovered = discover_network(data)\n\n# Compare with ground truth\nprint(f\"True edges: {true_network.number_of_edges()}\")\nprint(f\"Discovered edges: {discovered.number_of_edges()}\")\n```\n\n## Key Features\n\n- **Multiple Algorithms**: Standard, alternative, information lasso, and lasso variants of oCSE\n- **Flexible Information Estimators**: Gaussian, k-NN, KDE, geometric k-NN, and Poisson methods \n- **Statistical Rigor**: Permutation-based significance testing with comprehensive test coverage\n- **Synthetic Data**: Built-in generators for testing and validation\n- **Visualization**: Network plotting and analysis tools\n- **Performance**: Optimized implementations with parallel processing support\n\n## Mathematical Foundation\n\nThe algorithm uses **conditional mutual information** to quantify causal relationships:\n\n$$I(X; Y | Z) = H(X | Z) + H(Y | Z) - H(X, Y | Z)$$\n\nThis measures how much variable X tells us about variable Y, beyond what we already know from conditioning set Z.\n\n**Causal Discovery Rule**: Variable X causes Y if knowing X(t) significantly improves prediction of Y(t+1), even when controlling for all other relevant variables.\n\nThe algorithm implements a two-phase approach:\n1. **Forward Selection**: Iteratively adds predictors that maximize conditional mutual information\n2. **Backward Elimination**: Removes predictors that lose significance when conditioned on others\n\n## Documentation\n\n\ud83d\udcda **[Read the full documentation on ReadTheDocs](https://causationentropy.readthedocs.io/)**\n\n- **[API Reference](https://causationentropy.readthedocs.io/en/latest/api/)**: Complete function and class documentation\n- **[User Guide](https://causationentropy.readthedocs.io/en/latest/user_guide/)**: Detailed tutorials and examples\n- **[Theory](https://causationentropy.readthedocs.io/en/latest/theory/)**: Mathematical background and algorithms\n- **Examples**: Check the `examples/` and `notebooks/` directories\n- **Research Papers**: See the `papers/` directory for theoretical foundations\n\n### Local Documentation\n\nBuild documentation locally:\n```bash\ncd docs/\nmake html\n# Open docs/_build/html/index.html\n```\n\n## Contributing\n\nWe welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n## Citation\n\nIf you use this library in your research, please cite:\n\n```bibtex\n @misc{slote2025causationentropy,\n author = {Slote, Kevin and Fish Jeremie and Bollt, Erirk},\n title = {CausationEntropy: A Python Library for Causal Discovery},\n url = {https://github.com/kslote1/causationentropy},\n doi = {}\n }\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details.\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/kslote1/causationentropy/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/kslote1/causationentropy/discussions)\n- **Email**: kslote1@gmail.com\n\n## Acknowledgments\n\nThis work builds upon fundamental research in information theory, causal inference, and time series analysis.\nSpecial thanks to the open-source scientific Python community.\n\n[Original Code](https://github.com/jefish003/NetworkInference)\n\n## LLM Disclosure\n\nGenerative AI was used to help with doc strings, documentation, and unit tests.\n",
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