/UNN

Causal Neural Nerwork

Primary LanguageJupyter NotebookMIT LicenseMIT

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. AAAI-24 Supplements 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. ICLR 2024 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 ｜ ICLR 2024 | Generation Code🧑‍💻 ｜ Dataset Download

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

AAAI-24 | Code🧑‍💻

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

ICLR 2023 | Latest Version | Code🧑‍💻