Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Local Causal Structure Learning in the Presence of Latent Variables
Authors: Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 43,539 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,767 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world data validate the effectiveness and efficiency of our approach. To demonstrate the accuracy and efficiency of our algorithm, we compared the proposed MMB-by-MMB algorithm with global learning methods, such as PCstable (Colombo & Maathuis, 2014), FCI (Spirtes et al., 2000), and RFCI (Colombo et al., 2012) 4. We also compared with local learning methods, such as the MB-by-MB algorithm (Wang et al., 2014), the Causal Markov Blanket (CMB) algorithm (Gao & Ji, 2015), and the Gradie Nt-based LCS (Gra N-LCS) algorithm (Liang et al., 2023) 5. |
| Researcher Affiliation | Collaboration | Feng Xie 1 Zheng Li 1 Peng Wu 1 Yan Zeng 1 Chunchen Liu 2 Zhi Geng 1 1Department of Applied Statistics, Beijing Technology and Business University, Beijing, China 2Ling Yang Co.Ltd, Alibaba Group, Hangzhou, China. |
| Pseudocode | Yes | Algorithm 1 MMB-by-MMB; Algorithm 2 MMBalg(Pellet & Elisseeff, 2008) |
| Open Source Code | Yes | Our source code is available from https://github.com/ fengxie009/MMB-by-MMB. |
| Open Datasets | Yes | We select four networks ranging from low to high dimensionality: MILDEW, ALARM, WIN95PTS, and ANDES, containing 35, 37, 76, and 223 nodes, respectively6. The details of those networks can be found at https:// www.bnlearn.com/bnrepository/.; gene expression data from Wille et al. (2004) |
| Dataset Splits | No | The paper mentions 'Each experiment was repeated 100 times with randomly generated data' and sample sizes, but does not provide specific train/validation/test splits, their percentages, or cross-validation details for reproducibility. |
| Hardware Specification | Yes | All experiments were performed with Intel 2.90GHz and 2.89 GHz CPUs and 128 GB of memory. |
| Software Dependencies | No | The paper mentions software packages like 'causallearn' and 'pcalg', and uses the 'Total Conditioning (TC)' algorithm, but it does not specify exact version numbers for these software dependencies or other key components. |
| Experiment Setup | No | The paper describes the data generation process and selection of latent/target variables but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes) or concrete thresholds/parameters used for the conditional independence tests within the algorithm during experiments. |