Boosting Local Causal Discovery in High-Dimensional Expression Data

We study how well Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, is able to predict causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by ICP, 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.