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Lucas Janson presents "Recent Advances in Model-X Knockoffs", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, November 20, 2019

Abstract: Two years ago in this workshop I presented my work on model-X knockoffs, a method for high-dimensional variable selection that provides exact (finite-sample) control of false discoveries and high power as a result of its flexibility to leverage any and all domain knowledge and tools from machine learning to search for signal. In this talk, I will discuss two recent works that significantly advance the usability and generality of model-X knockoffs. First, I will show how the original assumptions of model-X knockoffs, that the multivariate distribution of the...

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Xiao-Li Meng presents "2020 Election and Privacy Protected Census: Data Quantity vs. Quality & Privacy vs. Utility", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, November 13, 2019
Abstract: The year 2020 will be a busy one for statisticians and more generally for data scientists; predictions about the 2020 US election are already underway. Will the lessons from the 2016 US election be learned, or will the prediction failure be repeated? How do we measure the quality of the data we rely upon for predictions? How small are our big data when we take their quality into account?  The US Census Bureau has announced that the data from the 2020 Census will be released under differential privacy protection, which – in layperson’s terms – means adding some... Read more about Xiao-Li Meng presents "2020 Election and Privacy Protected Census: Data Quantity vs. Quality & Privacy vs. Utility"
Nicole Pashley presents "Causal Inference for Multiple Non-Randomized Treatments using Fractional Factorial Designs", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, November 6, 2019
Abstract: We explore a framework for addressing causal questions in an observational setting with multiple treatments. This setting involves attempting to approximate an experiment from observational data. With multiple treatments, this experiment would be a factorial design. However, certain treatment combinations may be so rare that, for some combinations, we have no measured outcomes in the observed data. We propose to conceptualize a hypothetical fractional factorial experiment instead of a full factorial experiment and lay out a framework for analysis in this setting. We also... Read more about Nicole Pashley presents "Causal Inference for Multiple Non-Randomized Treatments using Fractional Factorial Designs"
Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 30, 2019
Abstract: This paper proposes a Bayesian synthetic control method (a.k.a., the latent multifactor model approach) for causal inference with observational time-series cross-sectional (TSCS) data. We develop a state-space latent factor model and make dynamic and multilevel extensions to the widely-applied difference-in-differences estimator, the synthetic control approach, and latent factor models. We adopt a fully Bayesian prior-to-posterior approach to parameter estimation and counterfactual prediction. Compared with existing frequentist approaches, our method has... Read more about Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage"
Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 30, 2019
Abstract: This paper proposes a Bayesian synthetic control method (a.k.a., the latent multifactor model approach) for causal inference with observational time-series cross-sectional (TSCS) data. We develop a state-space latent factor model and make dynamic and multilevel extensions to the widely-applied difference-in-differences estimator, the synthetic control approach, and latent factor models. We adopt a fully Bayesian prior-to-posterior approach to parameter estimation and counterfactual prediction. Compared with existing frequentist approaches, our method has... Read more about Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage"
Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 30, 2019
Abstract: This paper proposes a Bayesian synthetic control method (a.k.a., the latent multifactor model approach) for causal inference with observational time-series cross-sectional (TSCS) data. We develop a state-space latent factor model and make dynamic and multilevel extensions to the widely-applied difference-in-differences estimator, the synthetic control approach, and latent factor models. We adopt a fully Bayesian prior-to-posterior approach to parameter estimation and counterfactual prediction. Compared with existing frequentist approaches, our method has... Read more about Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage"
Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 30, 2019
Abstract: This paper proposes a Bayesian synthetic control method (a.k.a., the latent multifactor model approach) for causal inference with observational time-series cross-sectional (TSCS) data. We develop a state-space latent factor model and make dynamic and multilevel extensions to the widely-applied difference-in-differences estimator, the synthetic control approach, and latent factor models. We adopt a fully Bayesian prior-to-posterior approach to parameter estimation and counterfactual prediction. Compared with existing frequentist approaches, our method has... Read more about Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage"
Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage", at CGIS Knafel Building (K354) - 12-1:30 pm, Wednesday, October 30, 2019
Abstract: This paper proposes a Bayesian synthetic control method (a.k.a., the latent multifactor model approach) for causal inference with observational time-series cross-sectional (TSCS) data. We develop a state-space latent factor model and make dynamic and multilevel extensions to the widely-applied difference-in-differences estimator, the synthetic control approach, and latent factor models. We adopt a fully Bayesian prior-to-posterior approach to parameter estimation and counterfactual prediction. Compared with existing frequentist approaches, our method has... Read more about Xun Pang presents "A Bayesian Generalized Synthetic Control Approach for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With Hierarchical Shrinkage"
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...

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