Abstract: There is a long debate in experimental design between the classic randomization design of Fisher, Yates, Kempthorne, Cochran, and those who advocate deterministic assignments based on notions of optimality. In nonsequential trials comparing...
Abstract: Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about...
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...
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...
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...
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...
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...
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...
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...
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...