CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series
Authors: Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, Jinli Suo, Kunlun He, Qionghai Dai
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling. |
| Researcher Affiliation | Academia | 1Department of Automation, Tsinghua University 2Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS) 3Chinese PLA General Hospital |
| Pseudocode | No | The paper provides architectural diagrams (e.g., Figure 1) and mathematical formulations, but it does not include explicit pseudocode blocks or sections labeled as "Algorithm". |
| Open Source Code | Yes | Our code and supplementary materials is on https://github.com/ jarrycyx/UNN. |
| Open Datasets | Yes | Dream-3 (Prill et al. 2010) is a gene expression and regulation dataset widely used as causal discovery benchmarks (Khanna and Tan 2020; Tank et al. 2022). |
| Dataset Splits | No | For a fair comparison, we search the best hyperparameters for the baseline algorithms on the validation dataset, and test performances on testing sets for 5 random seeds per experiment. The paper mentions using a "validation dataset" but does not specify the exact split percentages or sample counts for training, validation, and test sets. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments, such as GPU/CPU models or other detailed computer specifications. |
| Software Dependencies | No | The paper mentions using Python and various libraries implicitly through its methodology (e.g., neural networks, Gumbel-Softmax), but it does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | No | The paper does not explicitly provide specific details about the experimental setup such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific optimizer settings in the main text. It mentions some details are in supplements but not in the main text. |