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].
On Learning Causal Models from Relational Data
Authors: Sanghack Lee, Vasant Honavar
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments conļ¬rm that RCDāis substantially more efļ¬cient than RCD with respect to its space and time requirements (see Figure 4). RCDātakes 70 seconds on average learning an RCM given h = 4 while RCD takes 50 minutes. |
| Researcher Affiliation | Academia | Sanghack Lee and Vasant Honavar Artiļ¬cial Intelligence Research Laboratory College of Information Sciences and Technology The Pennsylvania State University University Park, PA 16802 EMAIL |
| Pseudocode | Yes | Algorithm 1 RCDā: RCD-Light |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper states, 'We generated schemas with 3 entity and 3 binary relationship classes with 2 and 1 attribute classes per entity and relationship class, respectively, with random cardinality. Given the schema, we generated an RCM with 10 dependencies of length up to h and maximum degree of 3.' This indicates synthetic data generation, but no public access information or citation for a dataset is provided. |
| Dataset Splits | No | The paper describes the generation of synthetic models but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions 'RCDā (built on RCD codebase)' but does not specify any software names with version numbers. |
| Experiment Setup | Yes | We generated schemas with 3 entity and 3 binary relationship classes with 2 and 1 attribute classes per entity and relationship class, respectively, with random cardinality. Given the schema, we generated an RCM with 10 dependencies of length up to h and maximum degree of 3. We followed settings in Maier et al. (2013): (i) RCD uses AGGs whose hop length is limited to 2h for practical reasons; and (ii) AGGs with 2h is adopted as a CI oracle. |