Presentations

Victor Chernozhukov (MIT) - Gaussian Approximations, Bootstrap, and Z-estimators when p >> n Wednesday, October 22, 2014

Abstract: We show that central limit theorems hold for high-dimensional normalized means hitting high-dimensional rectangles. These results apply even when p>> n. These theorems provide Gaussian distributional approximations that are not pivotal, but they can be consistently estimated via Gaussian multiplier methods and the empirical bootstrap. These results are useful for building confidence bands and for multiple testing via the step-down methods. Moreover, these results hold for approximately linear estimators. As an...

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Teppei Yamamoto (MIT) - Design, Identification, and Sensitivity Analysis for Patient Preference Trials Wednesday, October 15, 2014

Authors: Dean Knox, Teppei Yamamoto, Matthew A. Baum, and Adam Berinsky

Abstract: Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its overall impact outside of the study sample. Patient...

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Alberto Abadie (Harvard Kennedy School) - Endogenous Stratification in Randomized Experiments- Presenter: Alberto Abadi Wednesday, October 8, 2014
Abstract: Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an index of baseline characteristics that predicts the values that individual outcomes would take on in the absence of the treatment. This article shows that substantial biases may arise in practice if the index is estimated, as is often the case, by...
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Jeff Golden (Harvard Medical School) - Computational Pathology: The heart, lungs…and brain of Precision Medicine - Presenter: Jeff Golden Wednesday, October 1, 2014

Abstract: Advances in high-throughput laboratory and health information technologies are revolutionizing the disciplines of pathology and laboratory medicine.  The ability to extract clinically actionable knowledge using computational methods from complex, high-dimensional laboratory and clinical (digital) data, thereby yielding more precise diagnoses, disease stratification, and selection of patient-specific treatments, will clearly be a significant and important realization in the delivery of health care. Pathologists, who are at the nexus of...

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Georg Gerber (Harvard Medical School) - The Dynamic Microbiome Wednesday, October 1, 2014

Abstract: The microbial communities, or microbiomes, residing in the mammalian gut are inherently dynamic, changing due to many factors including host maturation, alteration of the diet, and exchange of microbes with the environment or other hosts. Recent advances in high-throughput technologies in experimental biology, such as DNA sequencing, are enabling collection of unprecedented amounts of microbiome data. I will discuss both computational and experimental projects in my lab for analyzing and generating longitudinal microbiome datasets. In...

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Brandon Stewart (Harvard) - Latent Factor Regressions for the Social Sciences Wednesday, September 24, 2014

Abstract: I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiples types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although, related models have...

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James Lloyd (University of Cambridge) - The Automatic Statistician Wednesday, September 17, 2014

Abstract:  While it is becoming easier to collect and store all kinds of data, including personal medical data, scientific data, and commercial data, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. The automatic statistician project aims to build an artificial intelligence for data science, to help people make sense of their data and to uncover challenging research problems in automatic data...

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Matt Blackwell (Harvard) - Game-changers: Detecting shifts in the flow of campaign contributions Wednesday, September 10, 2014

Abstract: In this paper, I introduce a Bayesian model for detecting changepoints in a time-series of contributions to candidates over the course of a campaign. This game-changers model is ideal for campaign contributions data because it allows for overdispersion, a key feature of contributions data. Furthermore, while many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with their location. I demonstrate...

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Eric Chaney (Harvard) - The Medieval Origins of Comparative European Development: Evidence from the Basque Country Wednesday, September 3, 2014

Abstract: This paper investigates the present-day economic impact of medieval republican institutions along the historical borders of the Basque Country in Spain and France. I present evidence suggesting that medieval republican institutions have had a lasting effect: in Spain the drop in incomes along the Basque border is similar to that between the richest and poorest areas of the euro zone today. Using present-day and historical data, I investigate the mechanisms through which these medieval institutions have had enduring effects. Although I find evidence...

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Nathan Kallus (MIT) - Regression-Robust Designs of Controlled Experiments Wednesday, April 30, 2014

Abstract: Achieving balance between experimental groups is a cornerstone of causal inference. Without balance any observed difference may be attributed to a difference other than the treatment alone. In controlled/clinical trials, where the experimenter controls the administration of treatment, complete randomization of subjects has been the golden standard for achieving this balance because it allows for unbiased and consistent estimation and inference in the absence of any a priori knowledge or measurements. However, since estimator variance under complete...

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