Presentations

Edward Kao and Steven Smith present "Network Causal Inference on Social Media Influence Operations", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 31, 2018:

Abstract: Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative...

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Tyler J. VanderWeele presents "On the Promotion of Human Flourishing, with Methodological Reflections" Wednesday, October 24, 2018

Abstract: Many empirical studies throughout the social and biomedical sciences focus only on very narrow outcomes such as income, or a single specific disease state, or a measure of positive affect. Human well-being or flourishing, however, consists in a much broader range of states and outcomes, certainly including mental and physical health, but also encompassing happiness and life satisfaction, meaning and purpose, character and virtue, and close social relationships. The empirical literature from longitudinal, experimental, and quasiexperimental studies is reviewed...

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10/17 - Susan Murphy presents "Stratified Micro-Randomized Trials with Applications to Mobile Health", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 17, 2018

Abstract: Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been impacted by prior treatments. Furthermore the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided. Here we discuss our work on the design of a mobile health smoking...

Read more about 10/17 - Susan Murphy presents "Stratified Micro-Randomized Trials with Applications to Mobile Health"
10/3 - Naoki Egami presents "Causal Diffusion Analysis with Stationarity: How Hate Crimes Diffuse across Space", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 3, 2018
Abstract: Although social scientists have long been interested in the process through which ideas and behavior diffuse, the identification of causal diffusion effects, also known as peer effects, remains challenging. Many scholars consider the commonly used assumption of no omitted confounders to be untenable due to contextual confounding and homophily bias. To address this long-standing identification problem, I introduce a class of stationary causal directed acyclic graphs (DAGs), which represent the time-invariant...
Read more about 10/3 - Naoki Egami presents "Causal Diffusion Analysis with Stationarity: How Hate Crimes Diffuse across Space"
9/26 - Shihao Yang presents "Big data, Google, and infectious disease prediction: a statistical perspective", at 12-1:30 pm - K354 - CGIS Knafel Building, Wednesday, September 26, 2018

Abstract: Big data generated from the internet have great potential in tracking and predicting massive social activities, in particular infectious diseases, whose accurate real-time prediction could help public health officials make timely decisions to save lives. We introduce a model ARGO (AutoRegression with GOogle search data / AutoRegression with General Online data) that has successfully utilized publicly available Google search data, with/without cloud-based electronic health records, to estimate current and near-future influenza-like illness...

Read more about 9/26 - Shihao Yang presents "Big data, Google, and infectious disease prediction: a statistical perspective"
9/19/2018 - Aaron Kaufman presents "An Automated Method to Estimate Survey Question Bias" , at K354 - CGIS Knafel Building, Wednesday, September 19, 2018:

Abstract: Many survey researchers are interested in gauging public support for government policy, but there is strong evidence that a question’s wording affects responses to it. I develop the first automated and scalable method to predict the magnitude and direction of the partisan bias a question’s wording may impose on survey responses, and show using a series of survey experiments that it outperforms public opinion scholars in predicting that bias. Using a novel data set of almost one million survey questions from 1997 to 2017, I then examine trends in...

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9/12/2018 - Junming Huang presents "Quantifying Gender Inequality in Scientific Careers" , at 12-1:30 pm - K354 - CGIS Knafel Building, Wednesday, September 12, 2018
Abstract: Gender inequality in academic careers, documented across all disciplines and countries, extends beyond the fraction of women involved in research: compared to their male colleagues, women publish less over the course of their careers and their work acquires fewer citations. Yet, all existing evidence is limited to selected countries or disciplines, restricting our ability assess the roots and implications of the gender disparity. Here we analyzed a large corpus of scientific publications since 1900, identifying the gender and reconstructing the full publishing career...
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4/25/18 - Jeff Gill presents "Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data", at CGIS Knafel K354, Wednesday, April 25, 2018

Jeff Gill presents

 

Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data

Unseen grouping, often called latent clustering, is a common feature in social science data.  Subjects may intentionally or unintentionially group themselves in ways that complicate the statistical analysis of substantively important relationships. This work introduces a new model-based clustering design which incorporates two sources of heterogeneity.  The first source is a random effect that introduces substantively unimportant...

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