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..
On the Complexity of Identification in Linear Structural Causal Models
Authors: Julian Dörfler, Benito van der Zander, Markus Bläser, Maciej Liskiewicz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a purely theory based paper with no experiments. No data sets were used. No crowdsourcing or contract work was done. |
| Researcher Affiliation | Academia | Julian D orfler Saarland University Benito van der Zander University of L ubeck Markus Bl aser Saarland University Maciej Li skiewicz University of L ubeck |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | This paper does not contain experimental results. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers for experimental reproducibility as it does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |