B1060
Title: Perturbations and causality in Gaussian latent variable models
Authors: Armeen Taeb - ETH Zurich (Switzerland) [presenting]
Abstract: With observational data alone, causal inference is a challenging problem. The task becomes easier when having access to data from perturbing the underlying system, even when the perturbations are happening in an unspecific and non-randomized way. We provide results that enable causal discovery in this setting, and also allow for the presence of latent variables. In particular, we examine a perturbation model for interventional data over a collection of Gaussian variables. Given access to data arising from perturbations, we will introduce a regularized maximum-likelihood framework that determines the class of equally representative DAGs, and uniquely identifies the underlying causal structure under sufficiently heterogeneous data. We illustrate the effectiveness of our framework on synthetic data as well as real data involving California reservoirs.
