Boosting Local Causal Discovery in High-Dimensional Expression Data

Autor:	Versteeg, Philip, Mooij, Joris M.
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Methodology
Zdroj:	2019 IEEE Intl. Conf. Bioinf. and Biomed. (BIBM 2019) pp. 2599-2604
Druh dokumentu:	Working Paper
DOI:	10.1109/BIBM47256.2019.8983232
Popis:	We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm, we use an optional preselection method and two different statistical tests. Empirically, the resulting LCD estimator is seen to closely approach the accuracy of ICP, the state-of-the-art method, while it is algorithmically simpler and computationally more efficient. Comment: Accepted at BIBM / CABB 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.02505 Zobrazit plný text záznamu View this record from Arxiv