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..
Recovering Causal Structures from Low-Order Conditional Independencies
Authors: Marcel Wienöbst, Maciej Liskiewicz10302-10309
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare this algorithm to previous approaches in Section 6. Finally, we discuss our results in Section 7. |
| Researcher Affiliation | Academia | Marcel Wien obst, Maciej Li skiewicz Institute of Theoretical Computer Science, University of L ubeck, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: The LOCI algorithm computes the representation G for a DAG-representable set of CIs up to order k. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | No | The paper states how data is generated: 'An undirected graph with n nodes is drawn randomly. More precisely, each edge is present with probability d/(n 1), meaning every node has expected degree d. Afterwards, a topological ordering of the nodes is randomly chosen in order to obtain a DAG D from the generated graph. From this DAG we can read off all zeroand first-order independencies...' This describes a data generation process rather than the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper uses generated data for experimental analysis ('All values are the means of 100 independent trials') but does not specify explicit train/validation/test splits, as the focus is on recovering causal structures in an oracle model rather than typical predictive model training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the parameters for generating random DAGs for evaluation (e.g., 'random DAGs with n nodes and expected node degree d'), but it does not specify hyperparameters or system-level training settings for a model in the traditional sense, as it focuses on an algorithm for causal structure learning. |