causalflow-rust 0.1.7

High-performance Causal Inference engine powered by Rust

CausalFlow

High-performance Causal Inference engine powered by Rust and LLM-friendly Python interfaces.

Overview

CausalFlow provides a state-of-the-art Causal Forest (Generalized Random Forest) implementation designed for speed, accuracy, and interpretability. By leveraging Rust for core computations and PyO3 for seamless Python integration, CausalFlow enables researchers and data scientists to estimate Heterogeneous Treatment Effects (ITE) on large-scale datasets with minimal overhead.

Key Features

• 🦀 High-Performance Core: Implemented in Rust with zero-copy data access and Rayon-powered multi-threading.
• 🌲 Generalized Random Forest (GRF): Robust splitting criteria maximizing treatment effect variance.
• ⚖️ Honesty Property: Built-in data subsampling to ensure unbiased effect estimation and prevent overfitting.
• 🤖 LLM-Friendly:
  • Structured Summaries: Automatic natural language interpretation of results.
  • ASCII Tables: Clean terminal outputs for easy digestion by AI agents.
  • Type Hints: Full .pyi support for IDEs and LLM tool calling.
• 🛡️ Validation Guardrails: Built-in methods for causal robustness checks (cross-validation, time-series support).

Installation

Prerequisites

• Rust toolchain (cargo, rustc)
• Python 3.8+
• maturin (for building from source)

1. Installation

# Clone the repository
git clone https://github.com/taka-data/CausalFlow
cd CausalFlow

# Set up environment and install
python3 -m venv .venv
source .venv/bin/activate
pip install maturin numpy pandas scikit-learn
maturin develop

Quick Start

import numpy as np
import causalflow as cf
from sklearn.datasets import fetch_california_housing

# Load data
data = fetch_california_housing()
X, Y = data.data, data.target
T = (X[:, 0] > X[:, 0].mean()).astype(float) # Dummy treatment

# 1. Create Model
model = cf.create_model(
    features=X, 
    treatment=T, 
    outcome=Y, 
    feature_names=list(data.feature_names)
)

# 2. Estimate Effects
results = model.estimate_effects(X)
print(results)  # Displays a structured summary table & interpretation

# 3. Headless Visualization (for LLM/Chat UI)
viz_data = cf.plot_model(model, plot='graph')
print(f"```json:causal-plot\n{viz_data}\n```")

# 4. Validate Robustness
validation = model.validate(n_folds=5)
print(validation.message)

Headless Visualization concept

CausalFlow follows a "Headless Visualization" architecture. The Python SDK doesn't generate images directly (like Matplotlib). Instead, it outputs strictly structured JSON data intended for a Custom ChatGPT-like UI. This ensures the best design consistency and zero hallucination of data by the LLM.

• causalflow-core: Core Rust library for causal algorithms.
• py-causalflow: Python bindings using PyO3.
• causalflow-macros: Procedural macros for metadata generation.

Contributing

Please see CONTRIBUTING.md for details on our development workflow.

License

MIT License.