Causal Discovery in Systems with Feedback Cycles

Abstract: While causal relations are generally considered to be anti-symmetric, we often find that over time there are feedback systems such that a variable can have a causal effect on itself. Such “cyclic” causal systems pose significant challenges for causal analysis, both in terms of the appropriate representation of the system under investigation, and for the development of algorithms that attempt to infer as much as possible about the underlying causal system from statistical data. This talk will aim to provide some theoretical insights about when and how these challenges can be addressed, how interventional data can be used to aid the discovery challenge in systems with feedback and what the many open questions in this field are.
Biography: Frederick Eberhardt is Professor of Philosophy at Caltech. His primary research focus is on methods for causal discovery from statistical data, the use of experiments in causal discovery, the integration of causal inferences from different data sets and the philosophical issues at the foundations of causality and probability. Before coming to Caltech, he was Assistant Professor in the Philosophy-Neuroscience-Psychology program at Washington University in St Louis. He received his PhD from Carnegie Mellon.