This repository is a collection of trustworthy AI related works from Huawei Noah's Ark Lab.

The PCIC 2022 Causal Inference Competition has launched, looking forward to your participation！

Track 1: Causal Transfer Learning for Time Seiries Data

Track 2: Causal Inference and Recommendation

• A causal structure learning toolchain containing various functionalities related to causal learning and evaluation. A tech report describing the toolbox is available here.
• The package offers a number of causal discovery algorithms, most of which are gradient-based, hence the name: gradient-based Causal structure learning pipeline.

• Information and baselines for causality-related competitions arranged by Noah's Ark Lab.

• Code for generating synthetic datasets and real-world datasets that we have open-sourced.

• Research works related to causaility. We will continously add new methods here.
• Currently contains implementations of CausalVAE, GAE, and causal discovery with reinforcement learning.