ORC IAP Seminar 2019 - Caroline Uhler
ORC IAP Seminar 2019: Machine Learning and Operations Research
Caroline Uhler "Using Interventional Data for Causal Inference"
http://orc.mit.edu/events/orc-iap-sem...
Caroline Uhler
Associate Professor, MIT
Abstract
Large-scale interventional datasets are becoming available in various fields, most prominently in genomics and advertising. The availability of such data motivates the development of a causal inference framework that is based on observational and interventional data. We first characterize the causal relationships that are identifiable from interventional data. In particular, we show that imperfect interventions, which only modify (i.e., without necessarily eliminating) the dependencies between targeted variables and their causes, provide the same causal information as perfect interventions, despite being less invasive. Second, we present the first provably consistent algorithm for learning a causal network from a mix of observational and interventional data. We end by discussing applications of this causal inference framework to the estimation of gene regulatory networks.
Bio
Caroline Uhler joined the MIT faculty in 2015 and is currently the Henry L. and Grace Doherty associate professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd in high school mathematics education all from the University of Zurich. She obtained her PhD in statistics, with a designated emphasis in computational and genomic biology, from the University of California, Berkeley in 2011. She then spent a semester as a research fellow in the program on "Theoretical Foundations of Big Data Analysis" at the Simons Institute at UC Berkeley, postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is a Sloan Research Fellow and an elected member of the International Statistical Institute, and she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research focuses on mathematical statistics and computational biology, in particular on graphical models and causal inference with applications to gene regulation.