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 | Conference PDF | Archive PDF | Plain Text | 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. |