Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Boosting Causal Discovery via Adaptive Sample Reweighting
Authors: An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua
ICLR 2023 | Venue PDF | 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. |