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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
s-ID: Causal Effect Identification in a Sub-population
Authors: Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,618 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,285 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the S-ID problem. |
| Researcher Affiliation | Academia | 1School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland 2College of Management of Technology, EPFL, Lausanne, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1: A sound and complete algorithm for S-ID |
| Open Source Code | Yes | Our implementation is at https://github.com/amabouei/s-ID. |
| Open Datasets | No | This paper is theoretical and does not involve the use of datasets for training, validation, or testing. |
| Dataset Splits | No | This paper is theoretical and does not describe experimental validation with data splits. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for its research. |
| Software Dependencies | No | The paper mentions an implementation but does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |