Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Authors: Ruixuan Zhao, Xin He, Junhui Wang
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The advantage of the proposed method is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19. |
| Researcher Affiliation | Academia | Ruixuan Zhao EMAIL School of Data Science City University of Hong Kong Kowloon Tong, Kowloon, Hong Kong Xin He EMAIL School of Statistics and Management Shanghai University of Finance and Economics Shanghai, China Junhui Wang EMAIL Department of Statistics The Chinese University of Hong Kong Shatin, New Territory, Hong Kong |
| Pseudocode | Yes | The details of the developed DAG learning method is summarized in Algorithm 1. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions the implementation of other methods. |
| Open Datasets | Yes | We now apply TL to analyze the spread of COVID-19 based on the daily global confirmed cases collected by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, which is publicly available at https://github.com/CSSEGISandData/COVID-19. |
| Dataset Splits | No | The paper describes data generation schemes for simulated examples and pre-processing steps for the COVID-19 dataset, but does not specify training, testing, or validation splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions several R packages used for implementing various methods (e.g., 'R package highDLingam', 'R package CompareCausalNetworks', 'R package EqVarDAG', 'R package causalXtreme', 'R package pcalg'), but does not provide specific version numbers for these packages or the R environment itself, which is necessary for reproducibility. |
| Experiment Setup | Yes | TL adopts the graphical Lasso algorithm with a fixed regularization parameter as suggested in Section 5, and the independence test based on distance covariance measure. ... the significance level of independent tests in both TL and PC is set as αn = 0.01, and the least square estimation is also applied to estimate the connection strength of directed structures for MDirect, MMHC and PC. |