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 [1].
Causal Effect Identification in Cluster DAGs
Authors: Tara V. Anand, Adele H. Ribeiro, Jin Tian, Elias Bareinboim
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Appendix A.3 (Anand et al. 2023) has an experimental study evaluating the ability of CDAGs to accurately assess the identifiability of effects while requiring less domain knowledge for their construction. |
| Researcher Affiliation | Academia | 1Department of Biomedical Informatics, Columbia University 2Department of Computer Science, Columbia University 3Department of Computer Science, Iowa State University |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a specific public dataset for experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) as it focuses on theoretical contributions. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments, as its focus is theoretical. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, as its focus is theoretical. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, hyperparameters, or training configurations, as its focus is theoretical. |