UNN - Official Repository for Causal Neural Network

Overview

This repository provides our latest research on Causal Neural Network.

Algorithm	Summary	Paper	Code
CUTS	EM-Style joint causal graph learning and missing data imputation for irregular temporal data	ICLR 2023 Latest Version	Code
CUTS+	Increasing scalability of neural causal discovery on high-dimensional irregular data.	Accepted to AAAI-24 Supplements arXiv	Code
CausalTime Benchmark	A novel pipeline capable of generating realistic time-series along with a ground truth causal graph that is generalizable to different fields. Official Website.	arXiv	Code
REACT	A causal deep learning approach that combines neural networks with causal discovery to develop a reliable and generalizable model to predict a patient's risk of developing CSA-AKI within the next 48 hours.	medRxiv	Code

🏥 REACT: Ultra-efficient causal deep learning for Dynamic CSA-AKI Detection Using Minimal Variables

medRxiv | Code🧑‍💻

🍺 CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery

Official Website ｜ arXiv | Generation Code🧑‍💻 ｜ Dataset Download

🎄CUTS+: High-dimensional Causal Discovery from Irregular Time-series

arXiv | Code🧑‍💻

🚩 CUTS: Neural Causal Discovery from Irregular Time-Series Data

ICLR 2023 | Latest Version | Code🧑‍💻