Identifiability of Direct Effects from Summary Causal Graphs
Authors: Simon Ferreira, Charles K. Assaad
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a complete identifiability result which characterizes all cases for which the direct effect is graphically identifiable from a summary causal graph and gives two sound finite adjustment sets that can be used to estimate the direct effect whenever it is identifiable. |
| Researcher Affiliation | Collaboration | Simon Ferreira1,2, Charles K. Assaad1 1Easy Vista 2 ENS de Lyon simon.ferreira@ens-lyon.fr, cassaad@easyvista.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing concrete access to source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not conduct empirical experiments or use datasets, therefore, no dataset availability information is provided. |
| Dataset Splits | No | This is a theoretical paper that does not conduct empirical experiments or use datasets, therefore, no information on training/test/validation splits is provided. |
| Hardware Specification | No | This is a theoretical paper that does not involve experimental computation, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not involve experimental computation, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |