Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

arXiv:2310.18212v2 Announce Type: replace
Abstract: Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default values provided by a particular implementation of an algorithm. While there have been numerous studies on performance evaluation of causal discovery algorithms, how hyperparameters affect …

abstract algorithm algorithms art arxiv cs.lg difference hyperparameter machine machine learning nature performance prediction robustness role state stat.me type unsupervised