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jarrycyx authored Mar 28, 2024
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Expand Up @@ -6,7 +6,7 @@ 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](https://openreview.net/forum?id=UG8bQcD3Emv) <br> [Latest Version](CUTS/CUTS_ver0324_camera5.pdf) |[Code](CUTS/)
| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | Accepted to AAAI-24 <br> [Supplements](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf) <br> [arXiv](https://arxiv.org/abs/2305.05890)|[Code](CUTS_Plus/)
| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | [AAAI-24](https://ojs.aaai.org/index.php/AAAI/article/view/29034) [Supplements](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf) |[Code](CUTS_Plus/)
| 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.](https://www.causaltime.cc/) | [ICLR 2024](https://openreview.net/forum?id=iad1yyyGme) | [Code](CausalTime/)
| 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](https://www.medrxiv.org/content/10.1101/2023.12.04.23299332v1) | [Code](REACT/)

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<center><img src="CausalTime/github_files/arch_github.png" width="800px"></center>

## 🎄CUTS+: High-dimensional Causal Discovery from Irregular Time-series
[arXiv](https://arxiv.org/abs/2305.05890) | [Code🧑‍💻](CUTS_Plus/)
[AAAI-24](https://ojs.aaai.org/index.php/AAAI/article/view/29034) | [Code🧑‍💻](CUTS_Plus/)

<center><img src="CUTS_Plus/github_files/arch.png" width="800px"></center>

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