Student Posters Repository

Inferring non-mediated cause-effect relations from time series data is a central problem in the study of large scale systems, with applications in climate science, seismology, neuroscience, statistical mechanics, biophysics and even economics. For this reason many data-driven methods have been proposed to identify the underlying causal structure of complex systems. Most of these methods are either based on heuristics or provide guarantees of a correct inference in restrictive domains of application. We develop a novel methodology to infer non-mediated cause-effect relations in large scale systems from observational data. Our method combines two prominent and complimentary techniques for detecting causality: the method of the PC-algorithm and the method of Granger Causality. Further, we provide theoretically proven bounds where the application of our methodology gives correct results. We also find that the domain of application of the methodology is extensive: the exact causal structure is correctly inferred in any large scale system for which every feedback loop contains at least one non-negligible delay.