缅北强奸

Causal discovery procedures are popular methods for discovering causal structure across the physical, biological, and social sciences. However, most procedures for causal discovery only output a single estimated causal model or single equivalence class of models. We propose a procedure for quantifying uncertainty in causal discovery. Specifically, we consider linear structural equation models with non-Gaussian errors and propose a procedure which returns a confidence sets of causal orderings which are not ruled out by the data. We show that asymptotically, the true causal ordering will be contained in the returned set with some user specified probability.

Joint work with Sam Wang and Mathias Darton.

Mladen Kolar is Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Kolar鈥檚 research is focused on high-dimensional statistical methods, probabilistic graphical models, and scalable optimization methods, driven by the need to uncover interesting and scientifically meaningful structures from observational data. His research appears in journals such as the Journal of Machine Learning Research, the Annals of Statistics, the Journal of the Royal Statistical Society, the Journal of the American Statistical Association, Biometrika, and other outlets. Kolar also regularly presents his research at the top machine learning conferences, including Advances in Neural Information Processing Systems (NeurIPS) and the International Conference of Machine Learning (ICML). Kolar currently serves as associate editor for the Journal of Machine Learning Research, the Journal of Computational and Graphical Statistics, and the New England Journal of Statistics in Data Science.

Kolar was awarded a prestigious Facebook Fellowship in 2010 for his work on machine learning and network models. He spent a summer with Facebook鈥檚 ads optimization team working on a large-scale system for click-through rate prediction. Kolar earned his PhD in Machine Learning in 2013 from Carnegie Mellon University, as well as a diploma in Computer Engineering from the University of Zagreb. For his Ph.D. thesis work on 鈥淯ncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems,鈥� Kolar received from 2014 SIGKDD Dissertation Award honorable mention.

Outside of academia, Kolar enjoys chess, running, cycling, and hiking.

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