Metadata-Version: 2.4
Name: causal-toolkit-josh
Version: 0.1.0
Summary: A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners
Author-email: Josh <joshcarbajal21@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/joshcarbajal/causal_toolkit_josh
Project-URL: Repository, https://github.com/joshcarbajal/causal_toolkit_josh
Keywords: causal inference,statistics,machine learning,treatment effects
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: lightgbm>=3.3.0
Requires-Dist: patsy>=0.5.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=3.0.0; extra == "dev"
Dynamic: license-file

[![Tests](https://github.com/joshcarbajal/causal_toolkit_josh/workflows/Tests/badge.svg)](https://github.com/joshcarbajal/causal_toolkit_josh/actions)
# causal-toolkit-josh

A Python package for causal inference methods.

## Installation

```bash
git clone https://github.com/joshcarbajal/causal_toolkit_josh
cd causal-toolkit-josh
uv pip install -e .
```

## Usage

```python
from causal_toolkit_josh import calculate_ate_ci, ipw, doubly_robust
from causal_toolkit_josh import s_learner_discrete, t_learner_discrete
```

## Methods

- **rct.py** — `calculate_ate_ci()`, `calculate_ate_pvalue()`
- **propensity.py** — `ipw()`, `doubly_robust()`
- **meta_learners.py** — `s_learner_discrete()`, `t_learner_discrete()`, `x_learner_discrete()`, `double_ml_cate()`
