title: Controlling false positive selections in high-dimensional regression and causal inference.
abstract: Guarding against false positive selections is important in many applications. We discuss methods based on subsampling and sample splitting for controlling the expected number of false positives and assigning p-values. They are generic and especially useful for high-dimensional settings. We review encouraging results for regression, and we discuss new adaptations and remaining challenges for selecting relevant variables, based on observational data, having a causal or interventional effect on a response of interest.
authors: Seminar für Statistik, ETH Zürich, Zürich, Switzerland.
authors: Peter
authors: P
authors: Bühlmann
authors: Philipp
authors: P
authors: Rütimann
authors: Markus
authors: M
authors: Kalisch
mesh_terms: D015984:Causality; D005189:False Positive Reactions; D015233:Models, Statistical