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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...

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"

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