Description:

Audience Choice: Bayesian Workflow / Causal Generalization / Modeling of Sampling Weights

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

The audience is invited to choose among three possible talks:

Bayesian Workflow: The workflow of applied Bayesian statistics includes not just inference but also building, checking, and understanding fitted models.  We discuss various live issues including prior distributions, data models, and computation, in the context of ideas such as the Fail Fast Principle and the Folk Theorem of Statistical Computing.  We also consider some examples of Bayesian models that give bad answers and see if we can develop a workflow that catches such problems.  For background, see here:  http://www.stat.columbia.edu/~gelman/research/unpublished/Bayesian_Workflow_article.pdf

Causal Generalization: In causal inference, we generalize from sample to population, from treatment to control group, and from observed measurements to underlying constructs of interest.  The challenge is that models for varying effects can be difficult to estimate from available data.  We discuss limitations of existing approaches to causal generalization and how it might be possible to do better using Bayesian multilevel models.  For background, see here:  http://www.stat.columbia.edu/~gelman/research/published/KennedyGelman_manuscript.pdf and here:  http://www.stat.columbia.edu/~gelman/research/published/causalreview4.pdf and here:  http://www.stat.columbia.edu/~gelman/research/unpublished/causal_quartets.pdf

Modeling of Sampling Weights: A well-known rule in practical survey research is to include weights when estimating a population average but not to use weights when fitting a regression model—as long as the regression includes as predictors all the information that went into the sampling weights. But what if you don’t know where the weights came from? We propose a quasi-Bayesian approach using a joint regression of the outcome and the sampling weight, followed by poststratifcation on the two variables, thus using design information within a model-based context to obtain inferences for small-area estimates, regressions, and other population quantities of interest.  For background, see here:  http://www.stat.columbia.edu/~gelman/research/unpublished/weight_regression.pdf

Topic will be chosen live by the audience attending the talk.