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..
The Computational Complexity of Structure-Based Causality
Authors: Gadi Aleksandrowicz, Hana Chockler, Joseph Halpern, Alexander Ivrii
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,431 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,207 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 show that the complexity of computing causality under the updated definition is DP 2 -complete. ... We then show that deciding causality under the updated HP definition is DP 2 complete. ... We start by defining the problem formally. ... We are now ready to prove our main result. Theorem 4.4 Lcause and LBcause are DP 2 -complete. |
| Researcher Affiliation | Collaboration | Gadi Aleksandrowicz IBM Research Lab, Haifa, Israel ... Hana Chockler Department of Informatics, King s College, London, UK ... Joseph Y. Halpern Computer Science Department, Cornell University, Ithaca, NY, U.S.A. ... Alexander Ivrii IBM Research Lab, Haifa, Israel |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |