DoWhy MCP v2.0 - Rigorous Causal Inference Tools

Python 3.9+ DoWhy License: MIT Code style: 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.

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

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 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

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

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