Statistics Seminar Series

Biography: Stephen is a postdoctoral researcher with Michael I. Jordan in the Statistics and EECS departments at UC Berkeley. He works on developing methods to analyze modern scientific data sets, leveraging sophisticated black box models while providing rigorous statistical guarantees. More specifically, he works on high-dimensional statistics (especially false discovery rate control), statistical machine learning, conformal prediction and causal inference.

Previously, Stephen completed his Ph.D. in the Stanford Department of Statistics advised by Emmanuel Candès. His thesis introduced methods for conditional independence testing and false discovery rate control in genomics, and was honored to receive the Ric Weiland Graduate Fellowship and the Theodore W. Anderson Theory of Statistics Dissertation Award for this work. Before his Ph.D.,Stephen studied statistics and mathematics at Harvard University, and then lived abroad teaching mathematics at NYU Shanghai. 

Title: Distribution-Free, Risk-Controlling Prediction Sets.

Abstract: To enable valid statistical inference in prediction tasks, we show how to generate set-valued predictions for black-box predictors that control the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any distribution by using a holdout set to calibrate the size of the prediction sets. We demonstrate our procedure in five large-scale machine learning problems: (1) classification problems where some mistakes are more costly than others; (2) multi-label classification, where each observation has multiple associated labels; (3) classification problems where the labels have a hierarchical structure; (4) image segmentation, where we wish to predict a set of pixels containing an object of interest; and (5) protein structure prediction. 

Register here

Biography: Trained as a statistician, Elizabeth's primary research interests are in the development and use of methodology to better design and analyze the causal effects of public health and educational interventions. In this way, she hopes to bridge statistical advances and research practice, working with mental health and educational researchers to identify and solve methodological challenges.

Title: The need for, and challenges of, policy evaluation during the COVID-19 pandemic. 

Abstract: To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders, and numerous studies aim to estimate their effects. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements and variation in timing to estimate policy effects, including in the COVID-19 context.

However, disentangling policy effects from other factors can be challenging in general, nevermind in the context of a pandemic with spillover, infectious disease dynamics, and a wide variety of policies being enacted. This talk will provide an overview of policy evaluation methods, including event studies and policy trial emulation approaches, discuss some of the limitations of standard two-way-fixed-effects type models, and discuss some of the specific issues in using these methods during the pandemic. The work will be motivated using a stylized analysis of the impact of state-level stay-at-home orders on total coronavirus cases. A conclusion is that estimates from panel methods -- with the right data and careful modeling and diagnostics -- can help add to our understanding of many policies, though doing so is often challenging. 

Register here

Biography: Elizabeth is an Associate Professor in the Department of Biostatistics at Johns Hopkins University and founder of the COVID-19 Collaboration Platform.

Her research is in causal inference and epidemiologic methods. Broadly, she is interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met. Elizabeth has worked on characterizing the bias that results from misclassification, i.e. violations of the assumption that variables were measured accurately. She has also worked on semiparametric estimation of instrumental variables models, as these models are useful for certain violations of “no unmeasured confounding” assumptions. 

Title: Social network dependence, the replication crisis, and (in)valid inference. 

Abstract: In the first part of this talk, we show that social network dependence can result in spurious associations due to network dependence, potentially contributing to replication crises across the health and social sciences.  Researchers in these fields frequently sample subjects from one or a small number of communities, schools, hospitals, etc., and while many of the limitations of such convenience samples are well-known, the issue of statistical dependence due to social network ties has not previously been addressed. A paradigmatic example of this is the Framingham Heart Study (FHS). Using a statistic that we adapted to measure network dependence, we test for network dependence and for possible spurious associations in several of the thousands of influential papers published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious estimates of association and anticonservative uncertainty quantification due to unacknowledged network structure.  

But data with network dependence abounds, and in many settings researchers are explicitly interested in learning about social network dynamics.  Therefore, there is high demand for methods for causal and statistical inference with social network data. The second part of the talk describes recent work on causal inference for observational data from a single social network, focusing on (1) new types of causal estimands that are of interest in social network settings, and (2) conditions under which central limit theorems hold and inference based on approximate normality is licensed.

Register here

Biography: TBC

Title: TBC

Abstract: TBC

Biography: TBC

Title: TBC

Abstract: TBC