CausalGo: High-Performance Causal Discovery in Go

CausalGo is a high-performance implementation of the SURD algorithm (Sparse Unbiased Recursive Regression) for causal discovery in datasets. Based on the research published in Nature Communications, it provides 5-10x speedup compared to the original Python implementation.

🔍 Key Features

• 🚀 Extreme performance (optimized matrix operations, parallel processing)
• 📊 Faithful implementation of the SURD algorithm from the original paper
• 💾 Memory-efficient handling of large datasets
• 📈 Full Gonum integration for scientific computing in Go
• 🔌 Plugin architecture for custom regression models
• ✅ Comprehensive tests & benchmarks (validated against reference implementation)

⚙️ Installation

go get github.com/CausalGo/causalgo

🚀 Quick Start

package main

import (
    "fmt"
    "log"
    "math/rand"
    
    "github.com/CausalGo/causalgo/surd"
    "gonum.org/v1/gonum/mat"
)

func main() {
    // Create synthetic data (100 samples, 3 variables)
    data := mat.NewDense(100, 3, nil)
    for i := 0; i < 100; i++ {
        x := rand.Float64()
        data.Set(i, 0, x)
        data.Set(i, 1, x*0.8+rand.Float64()*0.2)
        data.Set(i, 2, x*0.5+data.At(i, 1)*0.5+rand.Float64()*0.1)
    }
    
    // Configure algorithm
    config := surd.Config{
        Lambda:    0.1,    // LASSO regularization
        Tolerance: 1e-5,   // Convergence threshold
        MaxIter:   1000,   // Maximum iterations
        Workers:   4,      // Use 4 CPU cores
    }
    
    // Initialize and run SURD
    model := surd.New(config)
    result, err := model.Fit(data)
    if err != nil {
        log.Fatalf("Fit error: %v", err)
    }
    
    // Display results
    fmt.Println("Causal Order:", result.Order)
    fmt.Println("Residual Variances:", result.Residuals)
    fmt.Println("Adjacency Matrix:")
    for _, row := range result.Adjacency {
        fmt.Println(row)
    }
}

System Requirements

• Go 1.24+ (recommended for best performance)
• Linux/macOS/Windows
• 4+ CPU cores for optimal performance

🧩 Architecture Overview

graph TD
    A[SURD Algorithm] --> B[Data Standardization]
    A --> C[Recursive Processing]
    C --> D[Worker Pool]
    D --> E[Regression Model]
    E -->|Default| F[LASSO]
    E -->|Custom| G[User Implementation]
    A --> H[Graph Construction]

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🔌 Using Custom Regression Models

Implement the Regressor interface and inject into SURD:

package main

import (
    "github.com/CausalGo/causalgo/regression"
    "gonum.org/v1/gonum/mat"
)

type CustomRegressor struct{}

// Fit implements the Regressor interface
func (r *CustomRegressor) Fit(X *mat.Dense, y []float64) []float64 {
    // Your custom regression implementation
    weights := make([]float64, X.RawMatrix().Cols)
    // ... training logic ...
    return weights
}

func main() {
    // ... setup data ...
    
    model := surd.New(surd.Config{Workers: 4})
    model.SetRegressor(&CustomRegressor{})
    
    result, err := model.Fit(data)
    // ... handle results ...
}

🧪 Testing

Run unit tests:

go test -v ./...

Run benchmarks:

go test -bench=. ./...

📊 Performance Benchmarks

Benchmarks on Intel i9-12900K (32GB RAM):

Dataset Size	Variables	Samples	Time/Op (ms)	Throughput
Small	10	100	12.5	80 op/s
Medium	50	1000	185	5.4 op/s
Large	100	5000	2,450	0.41 op/s

Detailed benchmarks

📚 Documentation & Packages

Full documentation available at pkg.go.dev.

Core Packages:

• surd: Main SURD implementation (New(), Fit(), Config)
• regression: Regression interfaces and implementations
  • Regressor interface
  • LASSO implementation
• graph: Causal graph results (GraphResult)

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature)
5. Create a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for details.

📝 Citation

If you use CausalGo in your research, please cite the original SURD paper:

@article{liu2024sparse,
  title={Sparse unbiased recursive regression for causal discovery},
  author={Liu, Weiyu and Cai, Ruichu and Hao, Zhen and Zhang, Kun},
  journal={Nature Communications},
  volume={15},
  pages={1-15},
  year={2024}
}

✉️ Contact

Project Maintainer: [Andrey Kolkov] - a.kolkov@gmail.com

Project Link: https://github.com/CausalGo/causalgo

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CausalGo - Unlocking causality at incredible speed! 🔓🚀