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 Identification under Markov equivalence: Calculus, Algorithm, and Completeness
Authors: Amin Jaber, Adele Ribeiro, Jiji Zhang, Elias Bareinboim
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical evaluation of CIDP is provided in the full report [Jaber et al., 2022]. Code is available at https://github.com/Causal AILab/PAGId |
| Researcher Affiliation | Academia | Amin Jaber Purdue University EMAIL Adèle H. Ribeiro Columbia University EMAIL Jiji Zhang Hong Kong Baptist University EMAIL Elias Bareinboim Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 IDP(P, x, y) and Algorithm 2 CIDP(P, x, y, z) are clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Causal AILab/PAGId |
| Open Datasets | No | The paper refers to an 'observational distribution P(V)' and 'observational data' but does not specify any particular datasets used for training models in the main text. An empirical evaluation is mentioned as being in a separate full report. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. While an empirical evaluation is mentioned, the details of data splits are not provided in this paper. |
| Hardware Specification | No | The paper does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies. |
| Experiment Setup | No | The paper does not contain specific details about experimental setup, such as hyperparameters or system-level training settings. |