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.
Intervention and Conditioning in Causal Bayesian Networks
Authors: Sainyam Galhotra, Joseph Halpern
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,953 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,504 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We do not have experimental results. |
| Researcher Affiliation | Academia | Sainyam Galhotra Computer Science Dept. Cornell University EMAIL Joseph Y. Halpern Computer Science Dept. Cornell University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We do not have experimental results. |
| Open Datasets | No | We do not have experimental results. |
| Dataset Splits | No | We do not have experimental results. |
| Hardware Specification | No | We do not have experiment results. |
| Software Dependencies | No | We do not have experiments. |
| Experiment Setup | No | We do not have experimental results. |