IDSS Distinguished Seminars

Please join us on Monday, November 1, 2021 at 4:00pm for the Distinguished Speaker Seminar with S. Craig Watkins (MLK Visiting Professor).

Please join us on Monday, December 6, 2021 at 4:00pm for the Distinguished Speaker Seminar with Dirk Bergemann (Yale University).

Please join us on Monday, April 4 at 4:00pm for the IDSS Distinguished Speaker Seminar with Mikhail Belkin (UC San Diego).

Please join us on Monday, May 2 at 4:00pm for the IDSS Distinguished Speaker Seminar with Elias Bareinboim (Columbia University).

Abstract: Sortition is a storied paradigm of democracy built on the idea of choosing representatives through lotteries instead of elections. In recent years this idea has found renewed popularity in the form of citizens’ assemblies, which bring together randomly selected people from all walks of life to discuss key questions and deliver policy recommendations. A principled approach to sortition, however, must resolve the tension between two competing requirements: that the demographic composition of citizens’ assemblies reflect the general population and…

Abstract: We develop new financial economics theory guided structural nonparametric methods for estimating conditional asset pricing models using deep neural networks, by employing time-varying conditional information on alphas and betas carried by firm-specific characteristics. Contrary to many applications of neural networks in economics, we can open the “black box” of machine learning predictions by incorporating financial economics theory into the learning, and provide an economic interpretation of the successful predictions obtained from neural networks,  by decomposing the neural predictors as…

Abstract: Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects. However, current methods for statistical inference on such data (a) make strong assumptions regarding the environment dynamics, e.g., assume the…

Abstract: We identify and reduce bias in the leading sample eigenvector of a high-dimensional covariance matrix of correlated variables. Our analysis illuminates how error in an estimated covariance matrix corrupts optimization. It may be applicable in finance, machine learning and genomics. Biography: Lisa Goldberg is Head of Research at Aperio and Managing Director at BlackRock.  She is Professor of the Practice of Economics at University of California, Berkeley, where she co-directs the Center for Data Analysis in Risk, an industry…