Federated Causal Discovery from Heterogeneous Data
Authors: Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method. |
| Researcher Affiliation | Academia | Loka Li1, Ignavier Ng2, Gongxu Luo1, Biwei Huang3, Guangyi Chen1,2, Tongliang Liu1, Bin Gu1, Kun Zhang1,2 1 Mohamed bin Zayed University of Artificial Intelligence 2 Carnegie Mellon University 3 University of California San Diego |
| Pseudocode | Yes | Algorithm 1 Fed CDH: Federated Causal Discovery from Heterogeneous Data |
| Open Source Code | Yes | The code is available at https://github.com/lokali/Fed CDH.git. Our source code has been appended in the Supplementary Materials. |
| Open Datasets | Yes | To evaluate our method, we conduct extensive experiments on synthetic datasets including linear Gaussian models and general functional models, and real-world dataset including f MRI Hippocampus (Poldrack et al., 2015) and HK Stock Market datasets (Huang et al., 2020). |
| Dataset Splits | No | The paper describes various experimental settings for evaluation (e.g., varying number of variables, clients, and samples), but does not explicitly mention a dedicated validation set or a train/validation/test split for model development or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. It only generally refers to computational time. |
| Software Dependencies | No | The paper mentions software like 'Causal-learn package' and 'Python' but does not specify their version numbers or other crucial software dependencies with specific versions. |
| Experiment Setup | Yes | We evaluate variable d {6, 12, 18, 24, 30} while fixing other variables such as K=10 and nk=100. We set client K {2, 4, 8, 16, 32} while fixing others such as d=6 and nk=100. We let the sample size in one client nk {25, 50, 100, 200, 400} while fixing other variables such as d=6 and K=10. We set the hyperparameter h to 5, and set the significance level for FCIT to 0.05. |