
December 2020
Mass Incarceration and the Challenge of Social Research
Bruce Western (Columbia University)
online
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.
Find out more »November 2020
An Introduction to Proximal Causal Learning
Eric Tchetgen Tchetgen (University of Pennsylvania)
online
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.
Find out more »October 2020
Social Networks and the Market for News
Rachel Kranton (Duke University)
online
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.
Find out more »May 2020
The Ethical Algorithm
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…
Find out more »April 2020
[POSTPONED] The Blessings of Multiple Causes
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…
Find out more »[POSTPONED] Guido Imbens – The Applied Econometrics Professor and Professor of Economics, Graduate School of Business, Stanford University
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.
Find out more »March 2020
Does Revolution Work? Evidence from Nepal
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…
Find out more »December 2019
Automating the Digitization of Historical Data on a Large Scale
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…
Find out more »November 2019
Causal Inference in the Age of Big Data
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…
Find out more »October 2019
Theoretical Foundations of Active Machine Learning
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…
Find out more »