causers 0.7.0

High-performance statistical operations for Polars DataFrames

causers

A high-performance statistical package for Polars (and pandas) DataFrames, powered by Rust.

🚀 Overview

causers provides blazing-fast statistical operations for both Polars and pandas DataFrames, leveraging Rust's performance through PyO3 bindings. Designed for data scientists and analysts who need production-grade performance without sacrificing ease of use.

✨ Key Features

• 🏎️ High Performance: Linear regression on 1M rows in ~250ms with HC3 standard errors
• 📊 Multiple Regression: Support for multiple covariates with matrix-based OLS
• 🔮 Logistic Regression: Binary outcome regression with Newton-Raphson MLE, fixed effects via Mundlak strategy
• 📈 Robust Standard Errors: HC3 heteroskedasticity-consistent standard errors included
• 🎯 Flexible Models: Optional intercept for fully saturated models
• 🏢 Clustered Standard Errors: Cluster-robust SE for panel/grouped data
• 🔄 Bootstrap Methods: Wild cluster bootstrap (linear) and score bootstrap (logistic)
• 📋 Two-Way Fixed Effects: Panel data regression with entity and time fixed effects
• 🧪 Synthetic DID: Synthetic Difference-in-Differences for causal inference with panel data
• 🎯 Synthetic Control: Classic SC with 4 method variants (traditional, penalized, robust, augmented)
• 🔬 Two-Stage Least Squares (IV): Instrumental variables estimation for causal inference with endogeneity
• 🤖 Double Machine Learning: Debiased/orthogonalized ML for causal inference with cross-fitting
• 🔧 Native Polars Integration: Zero-copy operations on Polars DataFrames
• 🐼 pandas Support: Seamlessly pass pandas DataFrames - automatic conversion with minimal overhead
• 🦀 Rust-Powered: Core computations in Rust for maximum throughput
• 🐍 Pythonic API: Clean, intuitive interface with full type hints
• 🌍 Cross-Platform: Works on Linux, macOS (Intel/ARM), and Windows

📦 Installation

From PyPI (Recommended)

pip install causers

# With pandas support
pip install causers[pandas]

From Source (Development)

# Prerequisites: Python 3.8+ and Rust 1.70+
git clone https://github.com/causers/causers.git
cd causers

# Install build dependencies
pip install "maturin>=1.4,<2.0" "polars>=0.52" numpy

# Build and install in development mode
maturin develop --release

Quick Start

For comprehensive examples demonstrating all causers functions with realistic data, see the notebook:

📓 examples/basic_examples.ipynb

The notebook includes:

Function	Description
dml()	Double Machine Learning
linear_regression()	OLS with clustered standard errors
logistic_regression()	Maximum likelihood with clustered SEs
two_stage_least_squares()	IV/2SLS for causal inference with endogeneity
synthetic_control()	Synthetic control method
synthetic_did()	Synthetic difference-in-differences

🛠️ Development

Prerequisites

• Python 3.8 or higher
• Rust 1.70 or higher
• Polars 0.52 or higher

Building from Source

# Clone the repository
git clone https://github.com/causers/causers.git
cd causers

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

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

# Build the Rust extension
maturin develop --release

Running Tests

# Install test dependencies (required for running tests)
pip install -e ".[test]"

# Run all tests with coverage
pytest tests/ --cov=causers --cov-report=html

# Run specific test categories
pytest tests/test_performance.py -v  # Performance benchmarks
pytest tests/test_edge_cases.py -v   # Edge case handling

# Run Rust tests
cargo test

Code Quality

# Format Python code
black python/ tests/

# Lint Python code
ruff check python/ tests/

# Type check
mypy python/

# Format Rust code
cargo fmt

# Lint Rust code
cargo clippy

📜 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Polars for the excellent DataFrame library
• PyO3 for seamless Python-Rust integration
• maturin for simplified packaging

📚 Resources

• Documentation
• API Reference
• GitHub Issues
• Discussions

🐛 Found a Bug?

Please open an issue with:

• Minimal reproducible example
• Expected vs actual behavior
• Environment details (OS, Python version, etc.)

---

Made with ❤️ and 🦀 by the causers team