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February 2018

Machine Learning and Causal Inference

February 6 @ 4:00 pm - 5:00 pm

Susan Athey (Stanford University)

MIT Building 32, Room 141

Abstract: This talk will review a series of recent papers that develop new methods based on machine learning methods to approach problems of causal inference, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments, environments with unconfoundedness, instrumental variables, and panel data will be considered. Bio: Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford,…

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December 2017

IDSS Distinguished Seminar – Essential Concepts of Causal Inference: A Remarkable History

December 12, 2017 @ 4:30 pm - 5:30 pm

Donald Rubin (Harvard University)

MIT Building 32, Room 141

  Abstract I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding, to me at least, was tied to…

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November 2017

Social Network Experiments – Nicholas Christakis (Yale University)

November 7, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

The Institute of Data, Systems, and Society host monthly talks by academic and industry leaders from around the world for the IDSS Distinguished Lecture series.

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October 2017

IDSS Distinguished Seminar Series: Latanya Sweeney (Harvard University)

October 3, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

How Technology Design will Dictate Our Civic Future

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September 2017

Fast and Slow Learning from Reviews

September 12, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

Speaker: Daron Acemoglu (MIT) Many online platforms present summaries of reviews by previous users. Even though such reviews could be useful, previous users leaving reviews are typically a selected sample of those who have purchased the good in question, and may consequently have a biased assessment. In this paper, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the…

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