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Past Events › IDSS Distinguished Seminar Series

A monthly lecture series featuring prominent global leaders and academics sharing research in areas that are impacted by the emergence of big data.

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May 2020

The Ethical Algorithm

May 19, 2020 @ 4:00 pm - 5:00 pm

Michael Kearns (University of Pennsylvania)

online

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…

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April 2020

[POSTPONED] The Blessings of Multiple Causes

April 13, 2020 @ 4:00 pm - 5:00 pm

David Blei (Columbia University)

E18-304

*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…

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[POSTPONED] Guido Imbens – The Applied Econometrics Professor and Professor of Economics, Graduate School of Business, Stanford University

April 7, 2020 @ 4:00 pm - 5:00 pm

E18-304

IDSS will host Prof. Guido Imbens as part of the Distinguished Speaker Seminar series. Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality, program evaluation, identification, Bayesian methods, semi-parametric methods, instrumental variables.

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March 2020

Does Revolution Work? Evidence from Nepal

March 3, 2020 @ 4:00 pm - 5:00 pm

Rohini Pande (Yale University)

E18-304

The last half century has seen the adoption of  democratic institutions in much of the developing world. However, the conditions under which de jure democratization leads to the representation of historically disadvantaged groups remains debated as do the implications of descriptive representation for policy inclusion. Using detailed administrative and survey data from Nepal, we examine political selection in a new democracy, the implications for policy inclusion and the role of conflict in affecting political transformation. I situate these findings in the context of…

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December 2019

Automating the Digitization of Historical Data on a Large Scale

December 2, 2019 @ 4:00 pm - 5:00 pm

Melissa Dell (Harvard University)

E18-304

https://youtu.be/mnM7ePr6xqM Over the past two centuries, we have transitioned from an overwhelmingly agricultural world to one with vastly different patterns of economic organization. This transition has been remarkably uneven across space and time, and has important implications for some of the most central challenges facing societies today. Deepening our understanding of the determinants of economic transformation requires data on the long-run trajectories of individuals and firms. However, these data overwhelmingly remain trapped in hard copy, with cost estimates for manual…

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November 2019

Causal Inference in the Age of Big Data

November 4, 2019 @ 4:00 pm - 5:00 pm

Jasjeet Sekhon (UC Berkeley)

E18-304

The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social, behavioral, and medical treatments. With the availability of fine-grained data, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges, Machine Learning (ML) is now being…

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October 2019

Theoretical Foundations of Active Machine Learning

October 7, 2019 @ 4:00 pm - 5:00 pm

Rob Nowak (University of Wisconsin - Madison)

E18-304

Title: Theoretical Foundations of Active Machine Learning Abstract: The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text, but they must be trained with more images and text than a person can see in nearly a lifetime.  The computational complexity of training has been offset by recent technological advances, but the cost of training data is measured in…

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September 2019

Selection and Endogenous Bias in Studies of Health Behaviors

September 30, 2019 @ 4:00 pm - 5:00 pm

Emily Oster (Brown University)

E18-304

Abstract: Studies of health behaviors using observational data are prone to bias from selection in behavior choices. How important are these biases? Are they dynamic - that is, are they influenced by the recommendations we make? Are there formal assumptions under which we can use information we have about selection on observed variables to learn about the possible bias from unobserved selection? About the Speaker: Emily Oster is a professor of economics. Prior to coming to Brown she was an…

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May 2019

Design and Analysis of Two-Stage Randomized Experiments

May 7, 2019 @ 4:00 pm - 5:00 pm

Kosuke Imai (Harvard University)

E18-304

Abstract: In many social science experiments, subjects often interact with each other and as a result, one unit's treatment can influence the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. In this talk, we will discuss two-stage randomized experiments, which enable the identification of the average spillover effects as well as that of the average direct effect of one's own treatment. In particular,…

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April 2019

A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar

April 2, 2019 @ 4:00 pm - 5:00 pm

Francesca Dominici (Harvard University)

E18-304

Abstract: What if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. We have developed an artificial neural network model that uses on-the-ground air-monitoring data…

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