Causal Discovery from Subsampled Time Series with Proxy Variables
Authors: Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our method on synthetic data and a real-world application, i.e., discovering causal pathways in Alzheimer s disease. ... We use the F1-score, precision, and recall, where precision and recall (resp.) measure the accuracy and completeness of identified causal edges, and F1 = 2 precision recall / precision+recall. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Peking University 2 Center on Frontiers of Computing Studies (CFCS), Peking University 3 School of Data Science, Fudan University 4 Yanjing Medical College, Capital Medical University 5 Institute for Artificial Intelligence, Peking University |
| Pseudocode | Yes | Algorithm 1: Discover the summary graph |
| Open Source Code | Yes | Our code is available at https://github.com/lmz123321/proxy_causal_discovery. |
| Open Datasets | Yes | We consider the Alzheimer s Disease Neuroimaging Initiative (ADNI) dataset [4], in which the imaging data is acquired from structural Magnetic Resonance Imaging (s MRI) scans. [4] Ronald Carl Petersen, Paul S Aisen, Laurel A Beckett, Michael C Donohue, Anthony Collins Gamst, Danielle J Harvey, Clifford R Jack, William J Jagust, Leslie M Shaw, Arthur W Toga, et al. Alzheimer s disease neuroimaging initiative (adni): clinical characterization. Neurology, 74(3):201 209, 2010. |
| Dataset Splits | No | The paper describes sample sizes for synthetic data and number of subjects for ADNI, but does not provide specific train/validation/test splits (e.g., percentages or exact counts for each split). |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using the 'causallearn package' for MAG recovery but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | The significance level is set to 0.05. For each graph, we generate temporal data with the structural equation Xi(t) = j PAi fij(Xj(t 1)) + Ni, where the function fij is randomly chosen from {linear,sin,tanh,sqrt}, the exogenous noise Ni is randomly sampled from {uniform,gauss.,exp.,gamma}. |