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..
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Authors: Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world personality data sets demonstrate the efficacy of the proposed approach in finite-sample cases. Our code will be publicly available. 6 EXPERIMENTS We validate our method using both synthetic and real-life data. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2University of California San Diego 3KDDI Research 4Mohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | Yes | Algorithm 1: The overall procedure for Rank-based Latent Causal Discovery (RLCD). |
| Open Source Code | No | Our code will be publicly available. |
| Open Datasets | Yes | real-world Big Five Personality dataset https://openpsychometrics.org/. |
| Dataset Splits | No | No specific information regarding training/validation/test dataset splits (e.g., percentages or sample counts) is provided in the paper. |
| Hardware Specification | Yes | We conduct all the experiments with single Intel(R) Xeon(R) CPU E5-2470. |
| Software Dependencies | No | Our code is implemented with Python 3.7. |
| Experiment Setup | Yes | the hyperparameter α is chosen from {0.1, 0.05, 0.01, 0.005} in favor of each method to ensure their best performance and thus a fair comparison. For the proposed method we employ α = 0.005 for the procedure of finding latent variables, while for the first stage we empirically find that using a rather big α would be better. |