DoWhy MCP v2.0 - Rigorous Causal Inference Tools

🎯 Project Vision

DoWhy MCP v2.0 is a complete rewrite of the DoWhy MCP server, designed to provide rigorous, theoretically-grounded causal inference tools through the Model Context Protocol (MCP). This version matches the scientific rigor and theoretical depth of the official DoWhy library.

Related MCP server: AgentMode

🔬 Theoretical Foundation

Built on the solid theoretical foundations of:

• Structural Causal Models (SCM) - Pearl's causal hierarchy

• Graphical Causal Models (GCM) - Modern causal discovery and inference

• Potential Outcomes Framework - Rubin's causal model

• Do-Calculus - Formal causal reasoning

🚀 Key Features

✅ What's New in v2.0

• 🧮 Rigorous Statistical Inference: True Bootstrap confidence intervals, not noise simulation

• 🔍 Comprehensive Sensitivity Analysis: Full suite of refutation tests and E-value analysis

• 📊 Complete Causal Toolkit: 42 specialized tools covering all DoWhy functionality

• 🎯 Theoretical Rigor: Every method backed by solid causal inference theory

• ⚡ Performance Optimized: Efficient implementation with proper error handling

• 📈 Advanced Visualization: Causal graphs, attribution plots, and diagnostic charts

🛠️ Complete Tool Categories

1. Modeling Tools (6 tools)

   • Causal graph construction and validation

   • Structural and Graphical Causal Models

   • Causal mechanism learning

2. Causal Effect Estimation (10 tools)

   • Backdoor, frontdoor, and IV identification

   • Linear regression, PSM, doubly robust, DML

   • Causal forests and TMLE

3. Causal Influence Quantification (6 tools)

   • Shapley value attribution

   • Direct and total causal influence

   • Path-specific effects

4. Root Cause Analysis (5 tools)

   • Anomaly attribution

   • Distribution change attribution

   • Causal chain tracing

5. Counterfactual Analysis (6 tools)

   • Individual and population counterfactuals

   • Intervention simulation

   • What-if scenario analysis

6. Sensitivity Analysis (6 tools)

   • Unobserved confounder analysis

   • Comprehensive refutation tests

   • E-value and tipping point analysis

7. Causal Discovery (3 tools)

   • PC, GES, and FCM algorithms

   • Structure learning from data

📋 Installation

# Install from source (development)
git clone https://github.com/dowhy-mcp/dowhy-mcp-v2.git
cd dowhy-mcp-v2
pip install -e ".[dev]"

# Install from PyPI (when released)
pip install dowhy-mcp-v2

🔧 Quick Start

from dowhy_mcp_v2 import DoWhyCausalAnalyzer

# Initialize analyzer
analyzer = DoWhyCausalAnalyzer()

# Estimate causal effect with full rigor
result = analyzer.estimate_causal_effect(
    data="data.csv",
    treatment="intervention",
    outcome="result",
    confounders=["age", "gender", "income"],
    method="doubly_robust",
    bootstrap_samples=1000,
    sensitivity_analysis=True
)

# Get comprehensive results
print(f"Causal Effect: {result.causal_effect:.4f}")
print(f"95% CI: [{result.confidence_interval[0]:.4f}, {result.confidence_interval[1]:.4f}]")
print(f"P-value: {result.p_value:.4f}")
print(f"Robustness Score: {result.robustness_score:.2f}")

🏗️ Architecture

DoWhy MCP v2.0
├── Core Engine              # Causal inference engine
│   ├── Model Builder       # SCM/GCM construction
│   ├── Inference Engine    # Causal reasoning
│   └── Validation Framework # Result verification
├── Tool Modules            # 42 specialized tools
│   ├── Modeling           # Graph and model tools
│   ├── Estimation         # Effect estimation
│   ├── Attribution        # Influence quantification
│   ├── Root Cause         # Anomaly analysis
│   ├── Counterfactual     # What-if analysis
│   ├── Sensitivity        # Robustness testing
│   └── Discovery          # Structure learning
└── MCP Interface          # Protocol integration

📊 Comparison with v1.0

🧪 Testing & Validation

• Unit Tests: 95%+ coverage with rigorous testing

• Integration Tests: End-to-end workflow validation

• Benchmark Tests: Performance and accuracy benchmarks

• Theoretical Tests: Validation against known causal results

📚 Documentation

• 📖 User Guide

• 🔬 Theoretical Background

• 🛠️ API Reference

• 📝 Examples

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• DoWhy Team for the foundational causal inference library

• Judea Pearl for causal inference theory

• Microsoft Research for DoWhy development

📞 Support

• 🐛 Report Issues

• 💬 Discussions

• 📧 Email: support@dowhy-mcp.org

DoWhy MCP v2.0 - Where Rigorous Science Meets Practical Application