Past Events › IDSS Distinguished Seminar Series

Please join us on Tuesday, April 6, 2021 at 3:00pm for the Distinguished Speaker Seminar with Jens Hainmueller (Stanford University).

Please join us on Tuesday, March 2, 2021 at 3:00pm for the Distinguished Speaker Seminar with Brigitte Madrian (Brigham Young University).

Please join us on Tuesday, February 2, 2021 at 3:00pm for the Distinguished Speaker Seminar with Nathan Nunn, Frederic E. Abbe Professor of Economics at Harvard University.

IDSS will host Prof. Bruce Western as part of the Distinguished Speaker Seminar series. Prof. Westerns research has examined the causes, scope, and consequences of the historic growth in U.S. prison populations. He is Co-Director of the Justice Lab at Columbia University.

Please join us on November 2, 2020 at 4pm for the Distinguished Speaker Seminar with Eric J. Tchetgen Tchetgen, Luddy Family President’s Distinguished Professor and Professor of Statistics at the University of Pennsylvania.

Please join us on October 5, 2020 at 4pm for the IDSS Distinguished Speaker Seminar with Rachel Kranton, James B. Duke Distinguished Professor of Economics at Duke University.

https://youtu.be/IATv0m5U5z8 Title: The Ethical Algorithm Abstract: Many recent mainstream media articles and popular books have raised alarms over anti-social algorithmic behavior, especially regarding machine learning and artificial intelligence. The concerns include leaks of sensitive personal data by predictive models, algorithmic discrimination as a side-effect of machine learning, and inscrutable decisions made by complex models. While standard and legitimate responses to these phenomena include calls for stronger and better laws and regulations, researchers in machine learning, statistics and related areas are…

*Please note: this event has been POSTPONED until Fall 2020* See MIT’s COVID-19 policies for more details.   Title: The Blessings of Multiple Causes Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal…