title: Methods for causal inference from gene perturbation experiments and validation.
abstract: Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.
authors.affiliation: Seminar for Statistics, Eidgenössische Technische Hochschule (ETH) Zurich, CH-8092 Zurich, Switzerland;
authors.forename: Nicolai
authors.initials: N
authors.lastname: Meinshausen
authors.affiliation: Department of Engineering and Information Technology, Bern University of Applied Sciences, CH-3400 Burgdorf, Switzerland;
authors.forename: Alain
authors.initials: A
authors.lastname: Hauser
authors.affiliation: Informatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands;
authors.forename: Joris M
authors.initials: JM
authors.lastname: Mooij
authors.affiliation: Max Planck Institute for Intelligent Systems, D-72076 Tuebingen, Germany.
authors.forename: Jonas
authors.initials: J
authors.lastname: Peters
authors.affiliation: Informatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands;
authors.forename: Philip
authors.initials: P
authors.lastname: Versteeg
authors.affiliation: Seminar for Statistics, Eidgenössische Technische Hochschule (ETH) Zurich, CH-8092 Zurich, Switzerland; buhlmann@stat.math.ethz.ch.
authors.forename: Peter
authors.initials: P
authors.lastname: Bühlmann
mesh_terms: D000465:Algorithms; D005434:Flow Cytometry; D017353:Gene Deletion; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae; D012984:Software; D013223:Statistics as Topic
keywords: genome database validation; graphical models; interventional–observational data; invariant causal prediction