Presentations

3/1/17 - Brendan Meade - A View of Compressed Sensing and Neural Network Emulation in Physical SciencesM, at CGIS Knafel K354, Wednesday, March 1, 2017

2/22/17 - Joseph Jay Williams - "Perpetually enhancing human learning through dynamic, personalized, collaborative experimentation", at CGIS Knafel K354, Wednesday, February 22, 2017

2/15/17 - Dean Eckles (MIT Sloan) - "Estimating peer effects in networks with peer encouragement designs", at CGIS Knafel K354, Wednesday, February 15, 2017

Dean Eckles (MIT Sloan) presents "Estimating peer effects in networks with peer encouragement designs"

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual....

2/8/17 - Michael Peress - Does Newspaper Coverage Mediate the Economic Vote?, at CGIS Knafel K354, Wednesday, February 8, 2017

Michael Peress presents "Does Newspaper Coverage Mediate the Economic Vote?"

Do voters punish incumbent governments when economic performance is poor or when the media report that economic performance is poor? We draw on an original data set of over 2 million newspaper articles reporting on the economy in 32 newspapers in 16 developed democracies over 32 years. We develop procedures for coding newspaper sentiment on the economy and apply our results to study the role of the media in driving the economic vote. Our results indicate voters respond directly to unemployment,...

2/1/17 - Paul von Hippel (UT-Austin) - Maximum likelihood multiple imputation: A more efficient approach to repairing and analyzing incomplete data, at CGIS Knafel K354, Wednesday, February 1, 2017

Paul von Hippel (UT- Austin) presents

Title: Maximum likelihood multiple imputation: A more efficient approach to repairing and analyzing incomplete data

Abstract: Maximum likelihood multiple imputation (MLMI) is a form of multiple imputation (MI) that imputes values conditionally on a maximum likelihood estimate of the parameters. MLMI contrasts with the most popular form of MI, posterior draw multiple imputation (PDMI), which imputes values conditionally on an estimate drawn at random from the posterior distribution of the parameters. Despite...

1/25/17 - Matt Taddy (Chicago Booth), at CGIS Knafel K354, Wednesday, January 25, 2017

Matt Taddy (Chicago Booth) presentation

Title: Counterfactual Prediction with Deep Instrumental Variables Networks

Abstract:

We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: models that map from observables to outputs via multiple layers of latent representations. These deep learning algorithms are effective tools for unstructured prediction, and they can be combined in AI systems to solve complex automated...

11/30/2016 - Christopher Rycroft - High-throughput screening of crystalline porous materials, at CGIS Knafel K354, Wednesday, November 30, 2016

High-throughput screening of crystalline porous materials

Chris Rycroft, Harvard University

Abstract: Crystalline porous materials, such as zeolites, contain complex networks of void channels that are exploited in many industrial applications, such as for carbon dioxide capture and storage. This talk will develop some geometry-based methods for statistically screening large databases of porous materials, to select candidates that are optimal for a given application.

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11/16/2016 - David Parkes - Long-term causal effects via behavioral game theory, at CGIS Knafel K354, Wednesday, November 16, 2016

Long-term causal effects via behavioral game theory

Random experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy on socio-economic platforms. One critical shortcoming of classical methods, however, is that they do not take into account the dynamic nature of response to policy changes and may fail to capture long-term effects. We formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies, using behavioral game theory and a latent space...

11/9/2016 - Sharon-Lise Normand - Assessing quality and equity in health care, at CGIS Knafel K354, Wednesday, November 9, 2016

ASSESSING QUALITY AND EQUITY IN HEALTH CARE

Sharon-Lise Normand

Department of Health Care Policy, Harvard Medical School

Department of Biostatistics, Harvard T.H. Chan School of Public Health

The last two decades have been characterized by an increasing focus on healthcare provider performance measures, most often utilizing multiple binary response outcomes. In this problem, data arise from multiple clusters where (a) outcomes within clusters are more similar than outcomes between...

11/2/2016 - Daniele Paserman (BU) - "Gender Differences in Cooperative Environments? Evidence from the U.S. Congress" (joint with Stefano Gagliarducci), at CGIS Knafel K354, Wednesday, November 2, 2016

The title of the presentation is:

"Gender Differences in Cooperative Environments? Evidence from the U.S. Congress" (joint with Stefano Gagliarducci)

Abstract: This paper uses data on bill sponsorship and cosponsorship in the U.S. House of Representatives to estimate gender differences in cooperative behavior. We employ a number of econometric methodologies to address the potential selection of female representatives into electoral districts with distinct preferences for cooperativeness, including regression discontinuity and matching. After...

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