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🧪 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.

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Key Features

🔄 Reproducibility

  • Complete workflows: from datasets to results to analysis to assets in published papers
  • Exact replication: Platform-agnostic deterministic replication of results including of randomisation and information obtained from LLMs

🔍 Transparency

  • Open source software: all software is open source, fully documented and tested on GitHub.
  • Open standard data formats: results and experiment metadata available in open standard formats e.g. JSON, GraphML, YAML which can therefore be processed by other software
  • Zenodo storage: all assets used in the complete workflow are publicly available on Zenodo

🤖 Automation

  • Ease of use: a single command can be used to replicate all the experiments and results for a given published paper.
  • Fine-grained control: alternatively, researchers can opt to look at, or replicate, individual elements of the workflow, for example, run a specific algorithm, or generate an individual chart
  • Dry-run capability: allows users to see time and resources required to replicate a whole paper or individual asset.
  • Efficiency: steps within workflows are run in parallel where possible, and users can opt to download results instead of regenerating them.

Integration with Ecosystem

  • 🔍 CausalIQ Discovery (causaliq-discovery) is called by this package to perform statistical structure learning.
  • 📊 CausalIQ Analysis (causaliq-predict) is called by this package to perform results analysis and generate assets for research papers.
  • 🔮 CausalIQ Predict (causaliq-predict) 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 created by the CausalIQ ecosystem.


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.