Equivalent Causal Models
Authors: Sander Beckers6202-6209
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 srekcebrednas@gmail.com |
| 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. |