Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Authors: Saptarshi Roy, Raymond K. W. Wong, Yang Ni
NeurIPS 2023 | Venue PDF | 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 EMAIL Raymond K. W. Wong Department of Statistics Texas A&M University College Station, TX 77843 EMAIL Yang Ni Department of Statistics Texas A&M University College Station, TX 77843 EMAIL |
| 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). |