From 8eaa590e3a0df20c590525cd04f66d1976291574 Mon Sep 17 00:00:00 2001
From: Yuxiao Cheng <46640740+jarrycyx@users.noreply.github.com>
Date: Mon, 9 Oct 2023 03:05:58 -0500
Subject: [PATCH] Update README.md
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| CUTS | EM-Style joint causal graph learning and missing data imputation for irregular temporal data | [ICLR 2023](https://openreview.net/forum?id=UG8bQcD3Emv)
[Latest Version](CUTS/CUTS_ver0324_camera5.pdf) |[Code](CUTS/)
| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | [arXiv](https://arxiv.org/abs/2305.05890) |[Code](CUTS_Plus/)
-| CausalTime | 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/) | [arXiv](https://arxiv.org/abs/2310.01753) | [Code](CausalTime/)
+| 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/) | [arXiv](https://arxiv.org/abs/2310.01753) | [Code](CausalTime/)
## 🍺 CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
-[arXiv](https://arxiv.org/abs/2310.01753) | [Code🧑💻](CausalTime/) ｜ [Official Website](https://www.causaltime.cc/)
+[Official Website](https://www.causaltime.cc/) ｜ [arXiv](https://arxiv.org/abs/2310.01753) | [Generation Code🧑💻](CausalTime/) ｜ [Dataset Download](https://www.causaltime.cc/dataset/)
