Boosting Local Causal Discovery in High-Dimensional Expression Data
Philip Versteeg, Joris M. Mooij · 2019
We study the performance of Local Causal Discovery (LCD) [5], 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 [13], 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.