lesong36/dowhy_mcp
If you are the rightful owner of dowhy_mcp and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
DoWhy MCP v2.0 is a comprehensive server for rigorous causal inference using the Model Context Protocol.
Modeling Tools
Tools for causal graph construction and validation, structural and graphical causal models, and causal mechanism learning.
Causal Effect Estimation
Tools for backdoor, frontdoor, and IV identification, linear regression, PSM, doubly robust, DML, causal forests, and TMLE.
Causal Influence Quantification
Tools for Shapley value attribution, direct and total causal influence, and path-specific effects.
Root Cause Analysis
Tools for anomaly attribution, distribution change attribution, and causal chain tracing.
Counterfactual Analysis
Tools for individual and population counterfactuals, intervention simulation, and what-if scenario analysis.
Sensitivity Analysis
Tools for unobserved confounder analysis, comprehensive refutation tests, and E-value and tipping point analysis.
Causal Discovery
Tools for PC, GES, and FCM algorithms and structure learning from data.
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.
🔬 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
-
Modeling Tools (6 tools)
- Causal graph construction and validation
- Structural and Graphical Causal Models
- Causal mechanism learning
-
Causal Effect Estimation (10 tools)
- Backdoor, frontdoor, and IV identification
- Linear regression, PSM, doubly robust, DML
- Causal forests and TMLE
-
Causal Influence Quantification (6 tools)
- Shapley value attribution
- Direct and total causal influence
- Path-specific effects
-
Root Cause Analysis (5 tools)
- Anomaly attribution
- Distribution change attribution
- Causal chain tracing
-
Counterfactual Analysis (6 tools)
- Individual and population counterfactuals
- Intervention simulation
- What-if scenario analysis
-
Sensitivity Analysis (6 tools)
- Unobserved confounder analysis
- Comprehensive refutation tests
- E-value and tipping point analysis
-
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
| 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
🤝 Contributing
We welcome contributions! Please see our for details.
📄 License
This project is licensed under the MIT License - see the 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:
DoWhy MCP v2.0 - Where Rigorous Science Meets Practical Application