Dr. Eberhardt's research interests lie at the formal end of philosophy of science, the machine learning end of statistics and computer science, and the learning and modeling end of 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. He has done some work on computational models in cognitive science and some historical work on the philosophy of Hans Reichenbach, especially his frequentist interpretation of probability.
Publications
- Eberhardt, Frederick;Lee, Lin-Lin (2022) Causal Emergence: When Distortions in a Map Obscure the TerritoryPhilosophies
- Eberhardt, Frederick (2022) A contemporary example of Reichenbachian coordinationSynthese
- Thompson, W. H.;Esteban, O. et al. (2021) Intracranial electrical stimulation alters meso-scale network integration as a function of network topology
- Dubois, Julien;Eberhardt, Frederick et al. (2020) Personality beyond taxonomyNature Human Behaviour
- Dubois, Julien;Oya, Hiroyuki et al. (2020) Causal Mapping of Emotion Networks in the Human Brain: Framework and Initial FindingsNeuropsychologia
- Beckers, Sander;Eberhardt, Frederick et al. (2019) Approximate Causal AbstractionProceedings of Machine Learning Research
- Zhalama, Mr.;Zhang, Jiji et al. (2019) ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
- Chalupka, Krzysztof;Perona, Pietro et al. (2018) Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
- Hyttinen, Antti;Plis, Sergey et al. (2017) A constraint optimization approach to causal discovery from subsampled time series dataInternational Journal of Approximate Reasoning
- Chalupka, Krzysztof;Eberhardt, Frederick et al. (2017) Causal Feature Learning: An OverviewBehaviormetrika