Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Authors: Saptarshi Roy, Raymond K. W. Wong, Yang Ni
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the superior performance of our method over existing methods in terms of causal graph estimation through extensive simulation studies. We also demonstrate the proposed method using a brain EEG dataset. |
| Researcher Affiliation | Academia | Saptarshi Roy Department of Statistics Texas A&M University College Station, TX 77843 roys8001@stat.tamu.edu Raymond K. W. Wong Department of Statistics Texas A&M University College Station, TX 77843 raywong@tamu.edu Yang Ni Department of Statistics Texas A&M University College Station, TX 77843 yni@tamu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, nor does it include clearly labeled algorithm sections or code-like formatted procedures. |
| Open Source Code | No | Code will be made available on the project s website on Github. |
| Open Datasets | Yes | We demonstrate the proposed FENCE model on a brain EEG dataset from an alcoholism study [Zhang et al., 1995]. |
| Dataset Splits | No | The paper describes simulation data generation parameters (n, p, d) and MCMC burn-in iterations but does not explicitly provide training/validation/test dataset splits or cross-validation details for empirical evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software packages like 'fdapace package in R', 'pcalg package in R', 'py-tetrad package in python', and 'eeglab toolbox of Matlab' but does not specify their version numbers. |
| Experiment Setup | Yes | For the implementation of the proposed FENCE, we fixed the number of mixture components to be 10 and ran MCMC for 5,000 iterations (discarding the first 2,000 iterations as burn-in and retaining every 5th iteration after burn-in). |