Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Authors: Aditya Chaudhry, Pan Xu, Quanquan Gu
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical results with experiments on both synthetic data and real-world climatological data. |
| Researcher Affiliation | Academia | 1Department of Mathematics, University of Virginia, Charlottesville, VA 22904, USA 2Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA. |
| Pseudocode | No | The paper describes methods algorithmically in text but does not include an explicitly labeled pseudocode or algorithm block/figure. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing the source code for the described methodology or provide links to a code repository. |
| Open Datasets | Yes | To demonstrate the applicability of our method to real-world data, we consider the climatological data set made available by Lozano et al. (2009). |
| Dataset Splits | No | The paper describes total number of observations (T) and other parameters for synthetic and real data, but does not provide explicit training, validation, or test dataset splits or how the data was partitioned for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions using R packages like 'flare', 'glmnet', and 'deseasonalize' but does not specify their version numbers. |
| Experiment Setup | No | The paper describes data generation parameters (T, d, p) and the structure of the model (first three lagged values, 3x3 grid), but it does not provide specific hyperparameter values (e.g., regularization parameter lambda, or the choice of mu for the debiased estimator). |