Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Causal Effect Identification in Cluster DAGs
Authors: Tara V. Anand, Adele H. Ribeiro, Jin Tian, Elias Bareinboim
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,764 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,540 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |