Causal and Statistical Inference; Human Learning; Hans Reichenbach's Frequentist Interpretation of Probability
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.