Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness
Authors: Amin Jaber, Adele Ribeiro, Jiji Zhang, Elias Bareinboim
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 jaber0@purdue.edu Adèle H. Ribeiro Columbia University adele@cs.columbia.edu Jiji Zhang Hong Kong Baptist University zhangjiji@hkbu.edu.hk Elias Bareinboim Columbia University eb@cs.columbia.edu |
| 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. |