huawei-noah/trustworthyAI

Health v2 · maintenance only

Adoption not part of the score

1132 stars · 249 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of trustworthy AI projects from Huawei Noah's Ark Lab, centered on gCastle, a causal structure learning toolchain with many gradient-based causal discovery algorithms. It also includes causality competition baselines, real-world and synthetic datasets, and research implementations such as CausalVAE and GAE.

Use cases

• learn causal structure from observational data
• run causal discovery algorithms like PC, GES, or NOTEARS
• evaluate estimated causal graphs against ground truth
• generate synthetic causal datasets for benchmarking
• train causal variational autoencoders
• get baselines for causality competitions

When to choose

• you need a Python toolbox with many causal discovery algorithms in one place
• you want gradient-based structure learning methods
• you need benchmark datasets and evaluation metrics for causal discovery

When to avoid

• you need full causal inference (treatment effect estimation) rather than structure learning
• you need production-grade distributed training infrastructure
• you work outside Python

Facets

library · maturity active

machine-learning data-science simulation machine-learning data-science python causal-discovery causal-inference causality structure-learning graph statistics independence-tests gcastle algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="huawei-noah/trustworthyAI")