Presentations

Xiang Zhou presents "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-Residuals Approach", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 23, 2019
Abstract: Analyses of causal mediation are often complicated by treatment-induced confounders of the mediator-outcome relationship. In the presence of such confounders, the natural direct and indirect effects of treatment on the outcome, into which the total effect can be additively decomposed, are not identified. An alternative but similar set of effects, known as randomized intervention analogues to the natural direct effect (R-NDE) and the natural indirect effect (R-NIE), can still be identified in this situation, but existing estimators for these effects require a... Read more about Xiang Zhou presents "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-Residuals Approach"
Pedro Rodriquez presents " Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 16, 2019

Abstract: We consider the properties and performance of word embeddings techniques in the context of political science research. In particular, we explore key...

Read more about Pedro Rodriquez presents " Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research"
Max Goplerud presents "Modelling Heterogeneity Using Bayesian Structured Sparsity", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 9, 2019
Abstract: How to estimate heterogeneity, e.g. the effect of some variable differing across observations, is a key question in political science. Methods for doing so make simplifying assumptions about the underlying nature of the heterogeneity to draw reliable inferences. This paper allows a common way of simplifying complex phenomenon (placing observations with similar effects into discrete groups) to be integrated into regression analysis. The framework allows researchers to (i) use their prior knowledge to guide which groups are permissible and (ii) appropriately... Read more about Max Goplerud presents "Modelling Heterogeneity Using Bayesian Structured Sparsity"
Santiago Olivella presents "Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 2, 2019
Abstract: Many social scientists theorize how various factors predict the dynamic process of network evolution. These theories explain the ways in which nodal and dyadic characteristics play a role in the formation and evolution of relational ties over time. We develop a dynamic model of social networks by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict the dynamic changes in the node membership of latent... Read more about Santiago Olivella presents "Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts"
Matt Blackwell presents "On Model Dependence in the Estimation of Interactive Effects", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, September 25, 2019

Abstract: Heterogeneous effects are ubiquitous in the social sciences and are often an important component of---and means of assessing---theoretical arguments. A common strategy to assess this heterogeneity is to include a single multiplicative interaction term between the treatment and a hypothesized effect moderator in a regression model. In this paper, we show how inferences about interactions under this approach are highly sensitive to modeling choices about how the effect modifier interacts with other covariates, an issue almost never discussed in practice.  We...

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Benjamin Lauderdale presents "Measuring Attitudes towards Public Spending using a Multivariate Tax Summary Experiment", at CGIS South Building (S001), Wednesday, September 18, 2019
**Note one-off location change**
 
Abstract: It is difficult to measure public views on tradeoffs between spending priorities because public understanding of existing government spending is limited and the budgetary problem is complicated.   We present a new measurement strategy using UK taxpayer summaries as the baseline for a continuous treatment, multivariate choice experiment.  The experiment proposes deficit neutral bundles of changes in spending and taxation, allowing us to investigate attitudes towards...
Read more about Benjamin Lauderdale presents "Measuring Attitudes towards Public Spending using a Multivariate Tax Summary Experiment"
Andrea Rotnitzky presents "Efficient adjustment sets for population average causal effect estimation in graphical models", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, September 11, 2019
Covariate adjustment is often used for estimation of population average causal effects (ATE). In recent years graphical rules have been derived for determining, from a causal diagram, all covariate adjustment sets. Restricting attention to causal linear models, a very recent article introduced two graphical criterions: one to compare the asymptotic variance of linear regression estimators that control for certain distinct adjustment sets and a second to identify the optimal adjustment set that provides the smallest asymptotic variance. In this talk, I will show that the same graphical... Read more about Andrea Rotnitzky presents "Efficient adjustment sets for population average causal effect estimation in graphical models"
Anja Sautmann presents "Adaptive treatment assignment in experiments for policy choice", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, September 4, 2019
Abstract: The goal of many experiments is to inform the choice between different policies. However, standard experimental designs are geared toward point estimation and hypothesis testing. We consider the problem of treatment assignment in an experiment with several non-overlapping waves, where the goal is to choose among a set of possible policies (treatments) for large-scale implementation. The optimal experimental design learns from earlier waves and assigns more experimental units to the better-performing treatments in later waves. We propose a computationally... Read more about Anja Sautmann presents "Adaptive treatment assignment in experiments for policy choice"
Michael Hughes presents "Discovering Disease Subtypes that Improve Treatment Predictions: Prediction-Constrained Topic Models for Personalized Medicine", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, May 1, 2019

Abstract: For complex diseases like depression, choosing a successful treatment from several possible drugs remains a trial-and-error process in current clinical practice. By applying statistical machine learning to the electronic health records of thousands of patients, can we discover subtypes of disease which both improve population-wide understanding and improve patient-specific drug recommendations? One popular approach is to represent noisy, high-dimensional health records as mixtures of low-dimensional subtypes via a probabilistic topic model. I will introduce...

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