Learning Cluster Causal Diagrams: An Information-Theoretic Approach
Authors: Xueyan Niu, Xiaoyun Li, Ping Li
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data support the effectiveness of the proposed method. |
| Researcher Affiliation | Industry | Xueyan Niu, Xiaoyun Li, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, WA 98004, USA {niuxueyan, lixiaoyun996, pingli98}@gmail.com |
| Pseudocode | Yes | Algorithm 1 Learning C-DAG representation; Algorithm 2 Greedy Search |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for their methodology is made publicly available. |
| Open Datasets | Yes | As an example of real-world application, we apply our method to the protein signaling dataset [Sachs et al., 2005], which contains the expression levels of n = 11 proteins and phospholipids in human immune system cells, with N = 7466 observations. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test splits for the datasets used in the experiments. It mentions N = 1000 data points for synthetic and N = 7466 observations for real data, but no specific splitting methodology. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions the 'pgmpy package in Python' and 'the traditional kNN-based non-parametric estimator [Kraskov et al., 2004]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In each scenario, we simulate N = 1000 data points and run Algorithm 1 with Z = 500 iterations. We set the parameter of the Erd os-R enyi graph ρ = 0.5 for random sampling and the channel noise parameters p1 = p2 = 0.1. We run our algorithms with ρ = 0.5 and Z = 500. The resulting C-DAG, shown in Figure 4b, complies with definition (2). In particular, the algorithm successfully discovered the two groups of closely related molecules, {Plcg, PIP3, PIP2} and {PKC, PKA, Jnk, Raf, P38, Mek, Erk, Akt}, in the biological process, as expected from the ground truth. |