Causal Inference in Complex Networks
Abstract
Causal determinism states that every event is necessitated by precedent events together with governing laws, natural or otherwise. Causal determinism is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. Yet determining statistical causation among interacting stochastic processes and variables remains quite challenging. This lecture will review recent advances in causal inference: How far have we come, and where do we go from here?
Short Biography
Negar Kiyavash is chair of Business Analytics in the College of Management of Technology at EPFL. Prior to joining EPFL, she was on faculty of Georgia Institute of Technology and University of Illinois at Urbana-Champaign. Her research focuses on data analysis with focus on causal inference in complex networks.
