CausalIQ Ecosystem Architecture¶

Overview¶

The CausalIQ ecosystem is designed as a modular framework for causal discovery research that seamlessly integrates statistical algorithms with Large Language Models (LLMs). The architecture emphasizes modularity, reproducibility, and human-AI collaboration to advance the field of causal inference and Bayesian network structure learning.

Architecture Principles¶

🔧 Modularity¶

Each project can be used independently, allowing researchers to adopt specific components without requiring the entire ecosystem.

🔗 Interoperability¶

Projects integrate seamlessly through standardized APIs, data formats, and shared configuration patterns.

🔬 Reproducibility¶

Open-source design with versioned datasets, experiment configurations, and results synchronized via Zenodo.

🤝 Human-AI Collaboration¶

Statistical causal discovery algorithms are enhanced by LLM reasoning for interpretation, direction inference, and domain knowledge integration.

Ecosystem Components¶

Core Projects¶

causaliq-papers (Datasets, experiments and results for papers)
├── causaliq-workflow (Orchestration & workflow management)
├── causaliq-discovery (Statistical algorithms & structure learning)
├── causaliq-score (Optimized scoring functions)
├── causaliq-analysis (Metrics & statistical analysis)
├── causaliq-knowledge (LLM integration & reasoning)  ⭐ (Previously causaliq-llm))
├── causaliq-core (Graphs, file i/o and utilities)  ⭐ NEW
└── zenodo-sync (Dataset & result synchronization)

Project Responsibilities¶

Project	Purpose	Key Features
causaliq-discovery	Core statistical algorithms	Bayesian network learning, score-based methods
causaliq-score	Graph scoring functions	Optimized BIC, AIC, BDeu implementations
causaliq-analysis	Result analysis	Metrics, stability analysis, graph comparison
causaliq-knowledge	LLM integration	Graph generation, causal direction inference
causaliq-workflow	Workflow orchestration	CI workflow inspired, DASK task management
causaliq-papers	Research outputs	Published configurations, datasets, results
causaliq-core	Shared code	Graph representations and utility functions
zenodo-sync	Data management	Automated synchronization with Zenodo

Data Flow Architecture¶

1. Input Stage¶

Raw datasets: Observational and experimental data
Domain knowledge: Expert-provided causal relationships
LLM priors: Generated initial graphs from metadata
Configuration: Experiment parameters and algorithm settings

2. Processing Stage¶

Statistical learning: Score-based structure learning algorithms
LLM guidance: Causal direction suggestions and constraint generation
Hybrid reasoning: Integration of statistical evidence with domain knowledge
Optimization: Graph search and scoring function evaluation

3. Evaluation Stage¶

Scoring: BIC, AIC, BDeu, and custom metrics
Stability analysis: Bootstrap sampling and edge confidence
Comparison: Against ground truth and baseline methods
Validation: Cross-validation and holdout testing

4. Output Stage¶

Learned graphs: Final Bayesian network structures
Metrics: Performance statistics and confidence measures
Interpretations: LLM-generated explanations and insights
Reports: Automated analysis summaries

5. Storage Stage¶

Version control: Git-based experiment tracking
Zenodo sync: Automated dataset and result archival
Metadata: Rich annotations for reproducibility

Integration Patterns¶

API Standards¶

Graph representation: NetworkX-compatible formats
Data interfaces: Pandas DataFrame standards
Configuration: YAML/JSON schema validation
Scoring: Consistent function signatures

Communication Protocols¶

Inter-project: REST APIs and message queues
LLM integration: OpenAI-compatible interfaces
Workflow: Dask distributed computing
Storage: Cloud-native object storage

Shared Data Structures¶

# Example graph representation
graph = {
    "nodes": ["X1", "X2", "X3"],
    "edges": [("X1", "X2"), ("X2", "X3")],
    "metadata": {
        "algorithm": "pc",
        "score": "bic",
        "confidence": 0.95
    }
}

LLM Integration Architecture¶

🧠 LLM Capabilities¶

Graph generation: Initial structures from domain descriptions
Causal direction: Arrow orientation suggestions
Interpretation: Natural language explanations
Constraint generation: Domain-informed restrictions
Report writing: Automated experimental summaries

🔄 Human-LLM Workflow¶

Human specification: Natural language experiment description
LLM parsing: Structured configuration generation
Statistical learning: Algorithm execution with LLM constraints
LLM interpretation: Results explanation and insights
Human validation: Expert review and refinement

Deployment Patterns¶

🔬 Research Environment¶

Local development: Individual project installation
Jupyter integration: Interactive analysis notebooks
Academic clusters: HPC job submission

☁️ Cloud Deployment¶

Container orchestration: Docker/Kubernetes
Serverless functions: Event-driven processing
Managed services: Cloud-native ML platforms

🏢 Enterprise Integration¶

API gateways: Secure external access
Data pipelines: ETL/ELT integration
Monitoring: Observability and logging

Quality Assurance¶

Testing Strategy¶

Unit tests: Individual function validation
Integration tests: Cross-project compatibility
Performance tests: Scalability benchmarks
Reproducibility tests: Result consistency validation

Documentation Standards¶

API documentation: OpenAPI specifications
User guides: Getting started tutorials
Developer docs: Contribution guidelines
Research papers: Algorithmic foundations

Future Extensions¶

Planned Enhancements¶

Real-time learning: Streaming data processing
Federated learning: Distributed causal discovery
Multi-modal data: Text, images, time series
Causal reasoning: Counterfactual inference

Research Directions¶

LLM fine-tuning: Domain-specific causal models
Active learning: Experimental design optimization
Uncertainty quantification: Bayesian model averaging
Scalability: Large-scale graph learning

This architecture document provides the blueprint for the CausalIQ ecosystem, enabling both human researchers and LLMs to understand and contribute to the framework's development and application.