An Efficient Maximal Ancestral Graph Listing Algorithm
Authors: Tian-Zuo Wang, Wen-Bo Du, Zhi-Hua Zhou
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical analysis demonstrates the superiority of our proposed method on efficiency and effectiveness. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China. Correspondence to: Zhi-Hua Zhou <zhouzh@lamda.nju.edu.cn>. |
| Pseudocode | Yes | Algorithm 1 Evaluating the third condition of Thm. 1; Algorithm 2 Updating a PMG with a valid local transformation of X represented by C; Algorithm 3 MAGLIST; Algorithm 4 BRUTEFORCE |
| Open Source Code | No | The paper mentions existing causality software packages (pcalg, causaldag) but does not provide a statement or link for the open-sourcing of the code for its own described methodology. |
| Open Datasets | No | The paper states 'We generate simulated PAGs' and 'we generate 100 Erdös-Rényi graph as the true DAGs' but does not use a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes simulation parameters ('number of vertices d {6, 8, 10, 12, 14, 16}', 'probability of an edge... ρ {0.1, 0.2, 0.3, 0.4, 0.5}') but does not specify training/validation/test dataset splits, as it does not involve training a machine learning model on data splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'causality software package such as pcalg [...] and causaldag [...]' but does not specify the version numbers of these or any other software dependencies used in their experiments. |
| Experiment Setup | Yes | We set the maximal running time for MAG listing given each PAG by 1800 seconds. For each parameter combination, we generate 100 Erdös-Rényi graph as the true DAGs. |