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.