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).

MS 5148
Rachel Wang
University of Sydney

For many decades Markov chain Monte Carlo (MCMC) methods have been the main workhorse of Bayesian inference. However, traditional MCMC algorithms are computationally intensive. In particular, the Metropolis-Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propose a general framework for performing MH-MCMC using two mini-batches (MHDB) of the whole dataset each time and show that this gives rise to approximately a tempered stationary distribution. We prove that MHDB preserves the modes of the original target distribution and derive an error bound on the approximation for a general class of models including mixtures of exponential family distributions, linear binary classification and regression. To further extend the utility of the algorithm to high dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities. We demonstrate the performance of our algorithm in neural network applications and show that compared with popular optimization methods, our method is more robust to the choice of learning rate and improves testing accuracy. (Joint work with Tung-Yu Wu and Wing H. Wong)

Physics and Astronomy Building Room 1434A
Guillaume Basse
UC Berkeley

Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent results on testing for two-stage experiments, which are powerful designs for assessing interference. In these designs, whole clusters (e.g., households, schools, or graph partitions) are assigned to treatment or control; then units within each treated cluster are randomly assigned to treatment or control. I demonstrate how to construct powerful tests for non-sharp null hypotheses and use these results to analyze a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. I discuss some extensions to more general forms of interference, as well as some current challenges. Paper here: https://arxiv.org/abs/1709.08036

I am currently a postdoctoral fellow in the Statistics Department at UC Berkeley where I am advised by Peng Ding. My research focuses on Causal Inference and Design of Experiments in the presence of interference. I got my PhD in Statistics at Harvard in 2018, under the supervision of Edo Airoldi. Before coming to Harvard I attended the Ecole Centrale Paris, where I studied Applied Mathematics and Engineering. I have lived in France, Israel, the US and Senegal, where I was born. I will start as an assistant professor in the MS&E and Statistics departments at Stanford in July 2019.