Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleSeptember 2024
Bounding the family-wise error rate in local causal discovery using Rademacher averages
Data Mining and Knowledge Discovery (DMKD), Volume 38, Issue 6Pages 4157–4183https://doi.org/10.1007/s10618-024-01069-0AbstractMany algorithms have been proposed to learn local graphical structures around target variables of interest from observational data, focusing on two sets of variables. The first one, called Parent–Children (PC) set, contains all the variables that ...
- research-articleAugust 2023
Bounding the family-wise error rate in local causal discovery using rademacher averages (extended abstract)
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceArticle No.: 726, Pages 6492–6497https://doi.org/10.24963/ijcai.2023/726Causal discovery from observational data provides candidate causal relationships that need to be validated with ad-hoc experiments. Such experiments usually require major resources, and suitable techniques should therefore be applied to identify candidate ...
- ArticleMarch 2023
Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages
Machine Learning and Knowledge Discovery in DatabasesPages 255–271https://doi.org/10.1007/978-3-031-26419-1_16AbstractMany algorithms have been proposed to learn local graphical structures around target variables of interest from observational data. The Markov boundary (MB) provides a complete picture of the local causal structure around a variable and is a ...