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Hierarchical Causal Models
Eli N. Weinstein, David M. Blei
Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patients, or cities in states. In the…
Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
Bohan Wu, Eli N. Weinstein, Sohrab Salehi +2
Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustw…
Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
Bohan Wu, Eli N. Weinstein, David M. Blei
Empirical Bayes (EB) improves the accuracy of simultaneous inference "by learning from the experience of others" (Efron, 2012). Classical EB theory focuses on latent variables that…
The Sequential Nature of Science: Quantifying Learning from a Sequence of Studies
Jonas M. Mikhaeil, Donald P. Green, David Blei
Scientific progress is inherently sequential: collective knowledge is updated as new studies enter the literature. We propose the sequential meta-analysis research trace (SMART), w…
Optimization-based Causal Estimation from Heterogenous Environments
Mingzhang Yin, Yixin Wang, David M. Blei
This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is th…