Standard methods of causal discovery take as input a statistical data set of
measurements of well-defined causal variables. The goal is then to determine
the causal relations among these variables. But how are these causal
variables identified or constructed in the first place? Often we have sensor
level data but assume that the relevant causal interactions occur at a
higher scale of aggregation. Sometimes we only have aggregate measurements
of causal interactions at a finer scale. I will motivate the general problem
of causal discovery and present recent work on a framework and method for
the construction and identification of causal macro-variables that ensures
that the resulting causal variables have well-defined intervention
distributions. Time permitting, I will show an application of this approach
to large scale climate data, for which we were able to identify the
macro-phenomenon of El Nino using an unsupervised method on micro-level
measurements of the sea surface temperature and wind speeds over the
Biography: Frederick Eberhardt is interested in the formal aspects of the philosophy of science, machine learning in statistics and computer science, and learning and modeling in psychology and cognitive science. His work has focused primarily 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. Eberhardt has done work on computational models in cognitive science and historical work on the philosophy of Hans Reichenbach, especially his frequentist interpretation of probability.
Before coming to Caltech, Eberhardt was an assistant professor in the Philosophy-Neuroscience-Psychology program and the Department of Philosophy at Washington University in St. Louis. In 2011, he took a two-year research leave to work on causal discovery methods at Carnegie Mellon University with a grant from the James S. McDonnell Foundation. Prior to his time at Washington University, he was a McDonnell Postdoctoral Fellow at the Institute of Cognitive and Brain Sciences at the University of California, Berkeley.