🧪 CausalIQ Research

CausalIQ Research is a curated collection of experimental setups, benchmark datasets, and published results that enable reproducible research and method comparison in the field of causal discovery and inference.

Early Development — This package has initial project scaffolding (CLI, docs, CI) but no completed feature releases yet. Release v0.1.0 (Graph Averaging) is in development.

Quick Links:

• 📖 Full Documentation
• 💻 Repository

Repository Structure

The repository is organised around research concepts rather than file types:

causaliq-research/
├── src/              # Minimal code (CLI, utilities)
├── models/           # Bayesian model specifications — by model
│   └── asia/
│   └── sachs/
├── experiments/      # Workflows + results — by experiment series
│   └── llm-benchmark-2026/
│       ├── workflow.yaml
│       └── results.db
├── papers/           # Generated assets — by paper
│   └── llm-priors-2026/
│       ├── tables/
│       └── figures/
└── scratch/          # Gitignored working directory

Key Features

🔄 Reproducibility

Complete workflows from datasets through results to published paper assets, with platform-agnostic deterministic replication including stable randomisation and cached LLM responses.

🔍 Transparency

All software is open source on GitHub, results use open standard formats (JSON, GraphML, YAML), and assets will be publicly available on Zenodo.

🤖 Automation

A single command replicates all experiments for a published paper, with fine-grained control over individual steps, dry-run capability, and parallel execution.

Integration with Ecosystem

• 🔍 CausalIQ Discovery (causaliq-discovery) is called by this package to perform statistical structure learning.
• 📊 CausalIQ Analysis (causaliq-analysis) is called by this package to perform results analysis and generate assets for research papers.
• 🔮 CausalIQ WhatIf (causaliq-whatif) is called by this package to perform causal prediction.
• 🔄 Zenodo Synchronisation (zenodo-sync) is used by this package to download datasets and upload results.
• 🧠 CausalIQ Knowledge (causaliq-knowledge) can be integrated into causal discovery, analysis and inference workflows to produce more accurate, transparent and interpretable results.
• 🤖 CausalIQ Workflow (causaliq-workflow) orchestrates the steps required for the reproduction of experiments, results, and published paper assets.

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CausalIQ Research enables reproducible, transparent causal discovery research by providing curated experimental setups, benchmark datasets, and published results that support method comparison and the advancement of reliable causal inference methodologies.