Bayesian Active Causal Discovery with Multi-Fidelity Experiments
Authors: Zeyu Zhang, Chaozhuo Li, Xu Chen, Xing Xie
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to demonstrate the effectiveness of our model. In this section, we conduct experiments to demonstrate the effectiveness of our model, where we focus on the following problems: (1) whether our model can achieve better performance than the previous ACD methods? (2) Whether the constraint in objective (10) in necessary? (3) How the DAG regularization coefficient influence the model performance? In the following, we first introduce the experiment setup, and then present and analyze the results. |
| Researcher Affiliation | Collaboration | Zeyu Zhang Gaoling School of Artificial Intelligence Renmin University of China zeyuzhang@ruc.edu.cn Chaozhuo Li Microsoft Research Asia cli@microsoft.com Xu Chen Gaoling School of Artificial Intelligence Renmin University of China xu.chen@ruc.edu.cn Xing Xie Microsoft Research Asia xingx@microsoft.com |
| Pseudocode | Yes | Algorithm 1: Algorithm of Licence for Single Intervention Scenario. Algorithm 2: Algorithm of Licence for Batch Intervention Scenario. |
| Open Source Code | No | The paper mentions 'the configuration file in our codes' but does not provide an explicit statement of code release or a link to a public repository for the methodology described. |
| Open Datasets | Yes | We experiment with three commonly used causal discovery datasets, including Erd os-Rényi graph (ER) [41], Scale-Free graph (SF) [42] and DREAM [43]. |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits. It mentions initial observational samples and then acquiring interventional data, but without precise percentages, counts, or predefined split references for reproduction. |
| Hardware Specification | Yes | Name Details CPU Intel Xeon Platinum 8350C 2.60GHz GPU RTX A5000 (24GB) Memory 42GB RAM |
| Software Dependencies | Yes | Name Details Python Version 3.8 Java Version 1.8.0 (Necessary for DREAM) |
| Experiment Setup | Yes | Table 2: The details of experimental settings. Name Explanation Value budget The total budget for interventional experiments, (i.e., C). 10/20/30/40/50 oracle number The number of oracles, (i.e., M) 3 oracle cost The cost for each oracle, (i.e., Λ) 2, 8, 32 oracle noise The extra additive noise for each oracle. 0.04, 0.02, 0.00 observation number The number of observational samples. 1000 expect edge number The number of expect edges. 2 additive noise The value of additive noise during data generations. 0.01. We carefully tune the hyper-parameters for baselines and our model, and the final values can be obtained in the configuration file in our codes. |