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..
On the Complexity of Counterfactual Reasoning
Authors: Yunqiu Han, Yizuo Chen, Adnan Darwiche
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,107 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,845 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally present empirical results in Section 7 which reveal that, on average, the complexity gap between counterfactual and associational/interventional reasoning on fully specified SCMs can be smaller than what our worst-case bounds may suggest. 7 Experimental Results We consider experiments that target random networks whose structures emulate the structures of SCMs used in counterfactual reasoning. |
| Researcher Affiliation | Academia | Yunqiu Han , Yizuo Chen , Adnan Darwiche University of California, Los Angeles EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Jointree to Twin Jointree |
| Open Source Code | No | No explicit statement providing access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | The paper describes methods for generating random networks ('We generated random networks according to the method used in [Darwiche, 2020].' and 'We also included experiments using random networks generated according to the method in [Ide and Cozman, 2002].'), but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset used for training or evaluation. |
| Dataset Splits | No | The paper describes generating random networks and evaluating their properties, but does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment were provided. |
| Experiment Setup | Yes | We used n {50, 75, 100, 125, 150, 200, 250, 300} and p {3, 5, 7}. For each combination of n and p, we generated 50 random, base networks and reported averages of two properties for the constructed jointrees: width and normalized width. We use the popular minfill heuristic [Kjaerulff, 1990] in our experiments. |