Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LLM-enhanced Score Function Evolution for Causal Structure Learning
Authors: Zidong Wang, Fei Liu, Qi Feng, Qingfu Zhang, Xiaoguang Gao
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations, conducted on discrete, continuous, and real datasets, demonstrate the high stability, generality and effectiveness of L-SFE. (Abstract) |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong, Hong Kong 2School of Electronic and Information, Northwestern Polytechnical University, Xi an, China EMAIL, EMAIL, EMAIL, EMAIL. |
| Pseudocode | Yes | To provide a clear understanding of L-SFE, we outline the pseudo-code in Alg. 1 in Supplementary material 1.2. |
| Open Source Code | Yes | Code is avaliable on https://github.com/wzd2502/L-SFE |
| Open Datasets | Yes | synthetic datasets generated from pytetrad are employed for training and testing. For the discrete dataset... For the continuous datasets... We present a case study using data from the real world COVID-19 pandemic in the UK... https://bayesian-ai.eecs.qmul.ac.uk/bayesys/ |
| Dataset Splits | No | The paper describes generating synthetic datasets for training and testing and mentions using a real-world COVID-19 dataset with 866 samples, but does not specify explicit training/test/validation splits (e.g., percentages or counts) for a single dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions "GPT-4o mini is utilized for score function discovery in L-SFE" and "synthetic datasets generated from pytetrad are employed", but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For the discrete dataset, L-SFE is trained on ten Random Graphs with n = 30... Each test is repeated 10 times with m = 5000. ... The GLS used for training is HC with a tabu search. |