s-ID: Causal Effect Identification in a Sub-population
Authors: Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {amir.abouei, ehsan.mokhtarian, negar.kiyavash}@epfl.ch |
| 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. |