Presentations

2/24/2016- Jessie Myers Franklin (Harvard & Brigham Women's)- Comparing marginal estimators of propensity-adjusted treatment effects in studies with few observed outcome events Wednesday, February 24, 2016

Title: Comparing marginal estimators of propensity-adjusted treatment effects in studies with few observed outcome events

Abstract:  Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, a typical study constructed from an electronic healthcare database, for example, administrative claims data, requires adjustment for many,...

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2/17/2016- Jann Spiess (Harvard)- Robust Post-Matching Inference Wednesday, February 17, 2016

Title: Robust Post-Matching Inference

Abstract: 

Nearest-neighbor matching (Cochran, 1953; Rubin, 1973) is a popular nonparametric tool to create balance between treatment and control groups in non-experimental data. As a preprocessing step for regression analysis, it reduces the dependence on parametric modeling assumptions (Ho et al., 2007). In this paper, we show how to obtain valid standard error estimates for linear regression after nearest-neighbor matching without replacement. We show that standard error estimates...

Read more about 2/17/2016- Jann Spiess (Harvard)- Robust Post-Matching Inference
2/10/2016- Hanna Wallach (Microsoft Research) Modeling Topic-Partitioned Network Structure Wednesday, February 10, 2016

Title: Modeling Topic-Partitioned Network Structure

Abstract: 

In this talk, I will discuss two projects centered around modeling
topic-partitioned network structure. The first focuses on obtaining
and analyzing local government email corpora. I will describe a field
experiment that we conducted to investigate whether governments'
compliance with public records requests is influenced by the knowledge
that their peers have already complied. I will then talk about
studying local government organizations...

Read more about 2/10/2016- Hanna Wallach (Microsoft Research) Modeling Topic-Partitioned Network Structure
2/3/2016- Nicole Immorlica (Microsoft Research)- The Degree of Segregation in Social Networks Wednesday, February 3, 2016

Abstract: In 1969, economist Thomas Schelling introduced a landmark model of racial segregation in which individuals choose residences based on the racial composition of the corresponding neighborhoods.  Simple simulations of Schelling's model suggest this local behavior can cause segregation even for racially tolerant individuals.  In this talk, we provide rigorous analyses of the degree of segregation in Schelling's model on one-dimensional and two-dimensional lattices.  We see that if agents refuse to live in neighborhood in which their...

Read more about 2/3/2016- Nicole Immorlica (Microsoft Research)- The Degree of Segregation in Social Networks
1/27/2016- David Lazer (Harvard)- Tools for 21st Century Social Science Wednesday, January 27, 2016

Abstract: Developments at the intersection of the social sciences, computer science, and the Internet have opened up new vistas for studying social systems. These opportunities often come with substantial start up costs. For example, the Internet enables experiments at larger scale/lower costs than was previously conceivable. However, the start up cost for managing/coding online experiments can still be substantial. I will discuss two data infrastructures that my lab has been working on.  The first is Volunteer Science (...

Read more about 1/27/2016- David Lazer (Harvard)- Tools for 21st Century Social Science
12/2/2015- Edo Airoldi (Harvard)- Estimating causal effects in the presence of interfering units Wednesday, December 2, 2015

Title: Estimating causal effects in the presence of interfering units

Abstract: Classical approaches to causal inference largely rely on the assumption of “lack of interference”, according to which the outcome of an individual does not depend on the treatment assigned to others. In many applications, however, such as designing and evaluating the effectiveness of healthcare interventions that leverage social structure, assuming lack of interference is untenable. In fact, the effect of interference itself is often an inferential target of...

Read more about 12/2/2015- Edo Airoldi (Harvard)- Estimating causal effects in the presence of interfering units
11/18/2015- Luke Miratrix (Harvard)- Estimating and assessing treatment effect variation in large-scale randomized trials with randomization inference Wednesday, November 18, 2015

Authors: Peng Deng, Avi Feller, Luke Miratrix

Abstract: Recent literature has underscored the critical role of treatment effect variation in estimating and understanding causal effects. This approach, however, is in contrast to much of the foundational research on causal inference; Neyman, for example, avoided such variation through his focus on the average treatment effect (ATE) and his definition of the confidence interval. We extend the Neymanian framework to explicitly allow both for treatment effect variation explained by covariates, known...

Read more about 11/18/2015- Luke Miratrix (Harvard)- Estimating and assessing treatment effect variation in large-scale randomized trials with randomization inference
11/11/2015- Manuel Gomez Rodriguez (Harvard)- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Wednesday, November 11, 2015

Title: COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Abstract: Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately,...

Read more about 11/11/2015- Manuel Gomez Rodriguez (Harvard)- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
11/4/2015- Albert-László Barabási (Northeastern)- Network Science: From structure to control Wednesday, November 4, 2015

Title: Network Science: From structure to control

Abstract: Systems as diverse as the world wide web, Internet or the cell are described by highly interconnected networks with amazingly complex topology. Recent studies indicate that these networks are the result of self-organizing processes governed by simple but generic laws, resulting in architectural features that makes them much more similar to each other than one would have expected by chance. I will discuss the order characterizing our interconnected world and its implications...

Read more about 11/4/2015- Albert-László Barabási (Northeastern)- Network Science: From structure to control

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