Individual Causal Structure Learning from Population Data
Authors: Wei Chen, Xiaokai Huang, Zijian Li, Ruichu Cai, Zhiyi Huang, Zhifeng Hao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real-world data demonstrate the correctness of the method even when the sample size of each individual s data is small. |
| Researcher Affiliation | Academia | 1School of Computer Science, Guangdong University of Technology, Guangzhou, China 2Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates 3Peng Cheng Laboratory, Shenzhen, China 4College of Mathematics and Computer Science, Shantou University, Shantou, China |
| Pseudocode | No | The paper describes the ICSL algorithm in Section 5 but does not provide a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its own source code or a direct link to a code repository for the implemented methodology. |
| Open Datasets | Yes | This f MRI dataset was acquired by a 3T scanner with TR= 2 s, resulting in a sample size of 160 [Sanchez-Romero et al., 2019] per subject. The raw data can be obtained from the Openf MRI project1. Our experiment uses the preprocessed data2. [...] We also applied our methods to Sachs data [Sachs et al., 2005]. [...] We also apply our algorithm to stock indices data that is collected from the Yahoo finance database for 5 years (from 2015 to 2019). |
| Dataset Splits | No | The paper mentions varying the 'sample size per individual' but does not specify the train/validation/test splits (e.g., percentages or exact counts) for the datasets used in experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper describes the methods and algorithms used but does not specify software dependencies with version numbers (e.g., Python version, library versions like PyTorch 1.9). |
| Experiment Setup | Yes | In detail, we vary the number of nodes with n = 6, 8, 10, 12, the sample size per individual with l = 50, 100, 500, 1000, the number of individuals m = 3, 5, 7, 9 and the number of different causal structures with d = 1, 2, 4, 8. The default parameters are marked as bold. The causal strength from one observed variable to another is randomly generated with the range of [0.5, 1.2]. Each setting was conducted 10 times and the average of the evaluation metrics was calculated as the final evaluation metric. |