Metadata-Version: 2.4
Name: causal_toolkit_tc
Version: 0.1.0
Summary: A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners
Author-email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/tc-git-1/causal_toolkit_tc
Project-URL: Documentation, https://github.com/tc-git-1/causal_toolkit_tc#readme
Project-URL: Repository, https://github.com/tc-git-1/causal_toolkit_tc
Project-URL: Bug Tracker, https://github.com/tc-git-1/causal_toolkit_tc/issues
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
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
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
Requires-Dist: packaging>=21.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=3.0.0; extra == "dev"
Requires-Dist: black>=22.0.0; extra == "dev"
Requires-Dist: pylint>=2.12.0; extra == "dev"
Requires-Dist: mypy>=0.950; extra == "dev"
Dynamic: license-file

[![Tests](https://github.com/tc-git-1/causal_toolkit_tc/workflows/Tests/badge.svg)](https://github.com/tc-git-1/causal_toolkit_tc/actions)

# causal_toolkit_tc

A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners.

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## 📦 Features
- **RCT Methods**: `calculate_ate_ci()`, `calculate_ate_pvalue()`
- **Propensity Score**: `ipw()`, `doubly_robust()`
- **Meta-Learners**: `s_learner_discrete()`, `t_learner_discrete()`, `x_learner_discrete()`, `double_ml_cate()`

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## ✅ Installation
```bash
git clone https://github.com/tc-git-1/causal_toolkit_tc.git
cd causal_toolkit_tc
uv pip install -e ".[dev]"
