Boosting Causal Discovery via Adaptive Sample Reweighting
Authors: An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets are carried out to validate the effectiveness of Re Score. |
| Researcher Affiliation | Academia | 1Sea-NEx T Joint Lab, 2National University of Singapore, 3Tsinghua University 4Renmin University of China, 5University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Re Score Algorithm for Differentiable Score-based Causal Discovery |
| Open Source Code | Yes | Our codes are available at https://github.com/anzhang314/Re Score. |
| Open Datasets | Yes | For a comprehensive comparison, extensive experiments are conducted on both homogeneous and heterogeneous synthetic datasets as well as a real-world benchmark dataset, i.e., Sachs (Sachs et al., 2005). |
| Dataset Splits | No | The paper mentions generating data for experiments and training on a specific number of samples for the Sachs dataset, but it does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | Yes | All Experiments are conducted on a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions the use of existing implementations (e.g., NOTEARS, GOLEM, NOTEARS-MLP) and specifies architectural details like ReLU activation and number of hidden layers, but it does not list specific version numbers for software dependencies such as Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | Detailed hyperparameter search space for different methods is shown in Table 4. |