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].
Equivalent Causal Models
Authors: Sander Beckers6202-6209
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The paper focuses on defining concepts, proposing definitions (e.g., Definition 1-15), and proving theorems (e.g., Theorem 1, Theorem 2) without conducting any empirical studies, experiments, or data analysis. |
| Researcher Affiliation | Academia | Sander Beckers Munich Center for Mathematical Philosophy Ludwig Maximilian University, Munich EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not mention or provide access to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments, thus no datasets for training are mentioned. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, thus no validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments; therefore, no software dependencies with version numbers are listed for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments or their setup, so no hyperparameters or training settings are provided. |