Architecture Overview¶

CausalIQ Ecosystem¶

causaliq-analysis is a component of the overall CausalIQ ecosystem architecture.

Package Purpose¶

causaliq-analysis provides tools for analysing and summarising results from causal discovery experiments:

Graph merging — Combine multiple learned graphs into probabilistic dependency graphs (PDGs)
Structural evaluation — Compute accuracy metrics against ground truth
Trace migration — Convert legacy experiment traces to modern formats

Key Architectural Concepts¶

Summarisation Paradigm¶

The summarisation paradigm provides a consistent pattern for aggregating experimental results across different dimensions. This enables workflows that:

Group results by network, sample size, algorithm, etc.
Filter inputs by metadata criteria
Apply metadata-driven weighting
Produce outputs with full provenance tracking

Graph Types¶

The package works with graph types from causaliq-core:

Type	Description
DAG	Directed acyclic graph with fully oriented edges
CPDAG	Completed partially directed acyclic graph (Markov equivalence class)
PDG	Probabilistic dependency graph with edge probabilities

Integration Points¶

Component	Integration
causaliq-core	Graph types, filter evaluation, weight computation
causaliq-workflow	Workflow actions, cache storage, matrix execution
causaliq-data	Dataset loading for evaluation

Module Structure¶

src/causaliq_analysis/
├── __init__.py         # Package exports
├── cli.py              # Command-line interface
├── graph.py            # Graph action enumerations
├── merge.py            # Graph merging to PDG
├── metrics.py          # Structural comparison metrics
├── migrate.py          # Trace migration utilities
├── trace.py            # Legacy Trace format support
├── validation.py       # Input validation
└── workflow_action.py  # Workflow action interface