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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Identifiability of Direct Effects from Summary Causal Graphs
Authors: Simon Ferreira, Charles K. Assaad
AAAI 2024 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |