Physics and Astronomy Building Room 1434A
Frauke Kreuter
University of Maryland

The recent reports of the Commission on Evidenced-Based Policymaking and the National Academy of Science Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods emphasize the need to make greater use of data from administrative and other processes. The promise of such data sources is great, and even more so if multiple data sources are linked in an effort to overcome the shortage of relevant information in each individual source. However, looking at countries in which administrative data have been accessible for longer, or the tech industry in which process data are used extensively for decision making, we see that process data are often insufficient to answer relevant questions or to ensure proper measurement. This creates a desire to augment process data and administrative data with surveys. This talk will focus on two practical aspects resulting from this situation: the enormous challenge in ensuring privacy, and the need to cross-train computers scientists, statisticians, and survey methodologists.

Professor Frauke Kreuter is Director of the Joint Program in Survey Methodology at the University of Maryland, USA; Professor of Statistics and Methodology at the University of Mannheim; and head of the Statistical Methods Research Department at the Institute for Employment Research in Nürnberg, Germany. She founded the International Program in Survey and Data Science, and is co-founder of the Coleridge Initiative. Frauke Kreuter is elected fellow of the American Statistical Association and recipient of the Gertrude Cox Award.

Physics and Astronomy Building Room 1434A
Jared Murray
University of Texas — Austin

We introduce a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with strong confounding. Our Bayesian causal forests model avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. This new parametrization also allows treatment heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively “shrink to homogeneity”, in contrast to existing Bayesian non- and semi-parametric approaches.

Jared is an assistant professor of statistics in the Departments of Statistics and Data Science and Information, Risk, and Operations Management at the McCombs School of Business. Until July of 2017 he was a visiting assistant professor in the Department of Statistics at Carnegie Mellon University. Prior to joining CMU he completed his Ph.D. in Statistical Science at Duke University, working with Jerry Reiter. His methodological work spans several areas, including Bayesian modeling for non- and semiparametric regression modeling, causal inference, missing data, and record linkage.

Physics and Astronomy Building Room 1434A
Speakers: TBA

Content: TBA

Physics and Astronomy Building Room 1434A
Adrian Raftery
University of Washington

The Intergovernmental Panel on Climate Change (IPCC) recently published climate change projections to 2100, giving likely ranges of global temperature increase for each of four possible scenarios for population, economic growth and carbon use. We develop a probabilistic forecast of carbon emissions to 2100, using a country-specific version of Kaya’s identity, which expresses carbon emissions as a product of population, GDP per capita and carbon intensity (carbon per unit of GDP). We use the UN’s probabilistic population projections for all countries, based on methods from our group, and develop a joint Bayesian hierarchical model for GDP per capita and carbon intensity in most countries. In contrast with opinion-based scenarios, our findings are statistically based using data for 1960–2010. We find that our likely range (90% interval) for cumulative carbon emissions to 2100 includes the IPCC’s two middle scenarios but not the lowest or highest ones. We combine our results with the ensemble of climate models used by the IPCC to obtain a predictive distribution of global temperature increase to 2100. This is joint work with Dargan Frierson (UW Atmospheric Science), Richard Startz (UCSB Economics), Alec Zimmer (Upstart), and Peiran Liu (UW Statistics).