SELF: Structural Equational Likelihood Framework for Causal Discovery
Authors: Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations using XGBoost validate the superiority of our proposal over state-of-the-art solutions, on both synthetic and real world causal structures. Experiment Settings To investigate the effectiveness and of genericity of SELF, the algorithms are tested on both linear non-Gaussian and nonlinear data, generated from synthetic and real world causal structures. |
| Researcher Affiliation | Collaboration | Ruichu Cai,1 Jie Qiao,1 Zhenjie Zhang,2 Zhifeng Hao1, 3 1 School of Computer Science, Guangdong University of Technology, China 2 Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore 3 School of Mathmatics and Big Data, Foshan University, China |
| Pseudocode | Yes | Algorithm 1 Hill-Climbing Based Causal Structure Search |
| Open Source Code | Yes | The implementation of SELF can be found in CRAN 1. https://cran.r-project.org/web/packages/SELF/index.html |
| Open Datasets | Yes | The algorithms are tested on both linear non-Gaussian and nonlinear data, generated from synthetic and real world causal structures. we explore the performance of the algorithms on four frequently used real world structures (Scutari 2009). The statistics of the structures are given in Table 2. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing, such as percentages or sample counts. It describes ranges for number of samples for synthetic data generation, but not how they are split for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It only mentions using 'XGBoost' which is a software framework. |
| Software Dependencies | No | The paper mentions software like 'XGBoost-GBLinear', 'XGBoost-GBTree', 'bnlearn package in R', and 'Compare Causal Networks package'. However, it does not provide specific version numbers for these software components, which is required for reproducible description. |
| Experiment Setup | No | While the paper has an 'Experiment Settings' section that describes parameters for synthetic data generation (number of variables, samples, average indegree), it does not provide specific hyperparameters or system-level training settings for the models used (e.g., learning rate, batch size, number of epochs for XGBoost). |