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].
Intervention and Conditioning in Causal Bayesian Networks
Authors: Sainyam Galhotra, Joseph Halpern
NeurIPS 2024 | Venue PDF | LLM Run Details
| 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. |