Abstract

Causal inference is a central pillar of many scientific disciplines. Motivated by applications in different scientific fields, there has been tremendous progress in causal inference in the last twenty years. At the same time, the era of big data has presented the field with massive, heterogeneous and complex data sets, posing further challenges. It is therefore crucial and timely to bring together the researchers from both ends, in order to share recent advances, to identify pressing problems, to spark productive collaborations, and to ultimately advance the state of the art by developing new theory and methods for causal inference with big data.