DoWhy MCP v2.0

# DoWhy MCP v2.0 - Rigorous Causal Inference Tools [](https://www.python.org/downloads/) [](https://github.com/py-why/dowhy) [](https://opensource.org/licenses/MIT) [](https://github.com/psf/black) ## 🎯 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. ## 🔬 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 ```bash # 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 ```python 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 | Feature | v1.0 | v2.0 | |---------|------|------| | Theoretical Rigor | Basic | ✅ Complete | | Bootstrap CI | ❌ Fake noise | ✅ True Bootstrap | | Sensitivity Analysis | ❌ Simplified | ✅ Comprehensive | | Causal Graphs | ❌ Limited | ✅ Full Support | | Tool Count | 4 basic | 42 rigorous | | Statistical Tests | ❌ Missing | ✅ Complete Suite | | Error Handling | ❌ Basic | ✅ Robust | | Documentation | ❌ Minimal | ✅ Comprehensive | ## 🧪 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](docs/user_guide.md) - [🔬 Theoretical Background](docs/theory/) - [🛠️ API Reference](docs/api/) - [📝 Examples](docs/examples/) ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - [DoWhy Team](https://github.com/py-why/dowhy) for the foundational causal inference library - [Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) for causal inference theory - [Microsoft Research](https://www.microsoft.com/en-us/research/) for DoWhy development ## 📞 Support - 🐛 [Report Issues](https://github.com/dowhy-mcp/dowhy-mcp-v2/issues) - 💬 [Discussions](https://github.com/dowhy-mcp/dowhy-mcp-v2/discussions) - 📧 Email: support@dowhy-mcp.org --- **DoWhy MCP v2.0** - Where Rigorous Science Meets Practical Application