Abstract

Co-authors: Dominik Rothenhäusler, Peter Bühlmann, Christina Heinze (ETH Zurich), and Jonas Peters (Univ of Copenhagen) I will present some ongoing work on two related projects. Firstly, some joint work with Jonas Peters, Dominik Rothenhaeulser and Peter Buhlmann on anchor regression, which is putting both standard regression and causal regression into a common framework and allows to interpolate between both in a continuous fashion. The anchor regression can be seen as a least-squares term with a causal, instrumental-variable-type, penalty. The chosen penalty is shown to correspond to the expected future magnitude of interventions and some risk bounds can be derived for the high-dimensional setting. Second, in joint work with Christina Heinze, I will show an application of related ideas to a (non-causal) setting in image classification.