Recovering Causal Structures from Low-Order Conditional Independencies
Authors: Marcel Wienöbst, Maciej Liskiewicz10302-10309
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {wienoebst, liskiewi}@tcs.uni-luebeck.de |
| 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. |