� CausalIQ Data Project¶

The CausalIQ Data project provides the data handling, statistical testing, and scoring infrastructure that causal discovery requires. It delivers plug-in data adapters, comprehensive score functions, and conditional independence tests used by structure learning algorithms.

Current Version: v0.3.0

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Key Features¶

🔌 Plug-in Data Adapters¶

Abstract Data base class with Pandas, NumPy, and Oracle (synthetic) adapters, supporting CSV I/O, data randomisation, and categorical/continuous variable types.

📊 Score Functions¶

Comprehensive scoring framework with entropy-based (BIC, AIC, log-likelihood), Bayesian (BDE, K2, BDJ, BDS), and Gaussian (BGE, BIC-g, loglik-g) score types, evaluated at node, DAG, or full network level.

🧪 Independence Tests¶

Chi-squared and Mutual Information tests for conditional independence (X ⊥ Y | Z), designed for constraint-based algorithms such as PC and FCI.

🔧 Preprocessing and Utilities¶

Data cleaning utilities, BNFit interface for Bayesian Network parameter fitting, and marginal computation.

Future Directions¶

🧩 Plugin Architecture¶

Third-party integration: Ability to use these data capabilities in third-party structure learning algorithms so that comparisons are based on a common scoring or conditional independence framework

🏛️ Stability Integration¶

Stable scores: Stable resolution of equal-score situations for unstable algorithms (e.g. Tabu)

🧠 LLM-assisted Causal Discovery¶

Data context: Data values and variable names as context for LLM-assisted causal discovery
Knowledge integration: Incorporation of LLM and human expertise in scores and priors via CausalIQ Knowledge

⚡ Optimised Performance¶

GPU data provider: Support for optimised data handling on GPU hardware
Intelligent data scanning: Reduce number of full-row data scans

🎲 Enhanced Distribution Support¶

Mixed types: Scores and independence tests supporting mixtures of continuous and categorical variables

Integration with CausalIQ Ecosystem¶

🔍 CausalIQ Discovery makes use of this package to provide objective functions and conditional independence tests for structure learning algorithms.
📊 CausalIQ Analysis uses score functions as part of the evaluation of learnt graphs.
💎 CausalIQ Core makes use of the BNFit interface to estimate parameters based on data.
🤖 CausalIQ Workflow uses the in-memory randomisation of this package for stability experiments.

CausalIQ Data provides the foundational data processing layer that enables robust, high-performance causal discovery through optimised scoring functions, conditional independence testing, and seamless integration across the entire CausalIQ ecosystem.