Скачать с ютуб On the Perils of Machine Learning with Biased Samples | Panous Toulis в хорошем качестве

On the Perils of Machine Learning with Biased Samples | Panous Toulis

In this talk, Panous Toulis from Booth School of Business of the University of Chicago, presents their research in causal Inference, especially networks, interference and spillovers. In addition, his research examines problems of "structured inference" where the basis of inference is on structural rather than analytical assumptions. This work emphasizes robustness, which is important in applications of social policy and fairness. This talk was presented at the Harvard Business School Crossing Disciplines: Studying Fairness, Bias, and Inequality in Management and Decision Sciences Research workshop on May 21, 2021. For more on the Digital Initiative, check out https://digital.hbs.edu​​.