statistical-causal-inference


Namestatistical-causal-inference JSON
Version 4.4.0 PyPI version JSON
download
home_pagehttps://github.com/rdmurugan/statistical-causal-inference
SummaryProduction-ready causal attribution and inference API with comprehensive monitoring, testing, and LLM integration
upload_time2025-10-07 01:40:50
maintainerNone
docs_urlNone
authorCausalMMA Team
requires_python>=3.9
licenseNone
keywords causal-inference machine-learning statistics llm artificial-intelligence performance scalability async vectorization
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # Statistical Causal Inference

Production-ready causal attribution and inference algorithms for high-performance applications.

## Overview

This package provides the core algorithms and statistical methods for causal inference, used by the CausalMMA SDK and other applications.

## Features

- **Statistical Causal Inference**: Advanced algorithms for causal effect estimation
- **Causal Discovery**: PC Algorithm, FCI, and other structure learning methods
- **Optimized Performance**: Vectorized operations with NumPy and Numba acceleration
- **Async Processing**: Efficient asynchronous computation with Dask
- **LLM Integration**: OpenAI integration for causal reasoning
- **Production Ready**: Comprehensive error handling and validation

## Installation

```bash
pip install statistical-causal-inference
```

### From source

```bash
git clone https://github.com/rdmurugan/statistical-causal-inference.git
cd statistical-causal-inference
pip install -e .
```

## Usage

```python
from causalinference.core.statistical_inference import StatisticalCausalInference, CausalMethod
from causalinference.core.statistical_methods import PCAlgorithm
import pandas as pd

# Statistical causal inference
sci = StatisticalCausalInference()
result = sci.estimate_causal_effect(
    data=df,
    treatment='treatment_column',
    outcome='outcome_column',
    method=CausalMethod.DOUBLY_ROBUST
)

# Causal discovery
pc = PCAlgorithm()
dag = pc.learn_structure(data=df)
```

## Core Modules

- `statistical_inference.py` - Causal effect estimation methods
- `statistical_methods.py` - PC Algorithm and other statistical methods
- `causal_discovery.py` - Structure learning algorithms
- `optimized_algorithms.py` - Performance-optimized implementations
- `async_processing.py` - Asynchronous computation utilities
- `llm_client.py` - LLM integration for causal reasoning

## Requirements

- Python >= 3.9
- NumPy >= 1.21.0
- Pandas >= 1.3.0
- Scikit-learn >= 1.0.0
- NetworkX >= 2.6.0
- SciPy >= 1.7.0

## Development

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Format code
black causalinference/
```

## License

Proprietary - For use in CausalMMA projects

## Contact

For questions or support, contact: durai@infinidatum.net

## Version

Current version: 4.4.0

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/rdmurugan/statistical-causal-inference",
    "name": "statistical-causal-inference",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": null,
    "keywords": "causal-inference, machine-learning, statistics, llm, artificial-intelligence, performance, scalability, async, vectorization",
    "author": "CausalMMA Team",
    "author_email": "durai@infinidatum.net",
    "download_url": "https://files.pythonhosted.org/packages/9e/7c/9ba8fadfc878bc7c76ca12758f7d3217bd1b0d7f5789987a052086cf9a7c/statistical_causal_inference-4.4.0.tar.gz",
    "platform": null,
    "description": "# Statistical Causal Inference\n\nProduction-ready causal attribution and inference algorithms for high-performance applications.\n\n## Overview\n\nThis package provides the core algorithms and statistical methods for causal inference, used by the CausalMMA SDK and other applications.\n\n## Features\n\n- **Statistical Causal Inference**: Advanced algorithms for causal effect estimation\n- **Causal Discovery**: PC Algorithm, FCI, and other structure learning methods\n- **Optimized Performance**: Vectorized operations with NumPy and Numba acceleration\n- **Async Processing**: Efficient asynchronous computation with Dask\n- **LLM Integration**: OpenAI integration for causal reasoning\n- **Production Ready**: Comprehensive error handling and validation\n\n## Installation\n\n```bash\npip install statistical-causal-inference\n```\n\n### From source\n\n```bash\ngit clone https://github.com/rdmurugan/statistical-causal-inference.git\ncd statistical-causal-inference\npip install -e .\n```\n\n## Usage\n\n```python\nfrom causalinference.core.statistical_inference import StatisticalCausalInference, CausalMethod\nfrom causalinference.core.statistical_methods import PCAlgorithm\nimport pandas as pd\n\n# Statistical causal inference\nsci = StatisticalCausalInference()\nresult = sci.estimate_causal_effect(\n    data=df,\n    treatment='treatment_column',\n    outcome='outcome_column',\n    method=CausalMethod.DOUBLY_ROBUST\n)\n\n# Causal discovery\npc = PCAlgorithm()\ndag = pc.learn_structure(data=df)\n```\n\n## Core Modules\n\n- `statistical_inference.py` - Causal effect estimation methods\n- `statistical_methods.py` - PC Algorithm and other statistical methods\n- `causal_discovery.py` - Structure learning algorithms\n- `optimized_algorithms.py` - Performance-optimized implementations\n- `async_processing.py` - Asynchronous computation utilities\n- `llm_client.py` - LLM integration for causal reasoning\n\n## Requirements\n\n- Python >= 3.9\n- NumPy >= 1.21.0\n- Pandas >= 1.3.0\n- Scikit-learn >= 1.0.0\n- NetworkX >= 2.6.0\n- SciPy >= 1.7.0\n\n## Development\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest tests/\n\n# Format code\nblack causalinference/\n```\n\n## License\n\nProprietary - For use in CausalMMA projects\n\n## Contact\n\nFor questions or support, contact: durai@infinidatum.net\n\n## Version\n\nCurrent version: 4.4.0\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "Production-ready causal attribution and inference API with comprehensive monitoring, testing, and LLM integration",
    "version": "4.4.0",
    "project_urls": {
        "Bug Tracker": "https://github.com/rdmurugan/statistical-causal-inference/issues",
        "Documentation": "https://github.com/rdmurugan/statistical-causal-inference",
        "Homepage": "https://github.com/rdmurugan/statistical-causal-inference",
        "Source Code": "https://github.com/rdmurugan/statistical-causal-inference"
    },
    "split_keywords": [
        "causal-inference",
        " machine-learning",
        " statistics",
        " llm",
        " artificial-intelligence",
        " performance",
        " scalability",
        " async",
        " vectorization"
    ],
    "urls": [
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "ef533266144d407b955066e182a5d29b432822b85fa69d827bb71d3f06ca8789",
                "md5": "7d2d0c5a32a7af15136d2b3b68e7176a",
                "sha256": "858b4469ad8b8c38039262dd6007d52d5987f13386a593082bf9c41df030c1ec"
            },
            "downloads": -1,
            "filename": "statistical_causal_inference-4.4.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "7d2d0c5a32a7af15136d2b3b68e7176a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9",
            "size": 83401,
            "upload_time": "2025-10-07T01:40:49",
            "upload_time_iso_8601": "2025-10-07T01:40:49.360776Z",
            "url": "https://files.pythonhosted.org/packages/ef/53/3266144d407b955066e182a5d29b432822b85fa69d827bb71d3f06ca8789/statistical_causal_inference-4.4.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "9e7c9ba8fadfc878bc7c76ca12758f7d3217bd1b0d7f5789987a052086cf9a7c",
                "md5": "cd7da74e1ca4de7ab1a6a80261311eeb",
                "sha256": "bce29b8abdab46e0368d74bb3bcb164e0f49d8a0c2bc16bab9d22a086a703728"
            },
            "downloads": -1,
            "filename": "statistical_causal_inference-4.4.0.tar.gz",
            "has_sig": false,
            "md5_digest": "cd7da74e1ca4de7ab1a6a80261311eeb",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 72721,
            "upload_time": "2025-10-07T01:40:50",
            "upload_time_iso_8601": "2025-10-07T01:40:50.794039Z",
            "url": "https://files.pythonhosted.org/packages/9e/7c/9ba8fadfc878bc7c76ca12758f7d3217bd1b0d7f5789987a052086cf9a7c/statistical_causal_inference-4.4.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-10-07 01:40:50",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "rdmurugan",
    "github_project": "statistical-causal-inference",
    "github_not_found": true,
    "lcname": "statistical-causal-inference"
}
        
Elapsed time: 1.69517s