Professor of Statistics and Computer Science at Columbia University
Banatao Auditorium | 310 Sutardja Dai Hall
September 29, 2023, 2-3PM PST
Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on “flat” models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data—replacing a flat causal model with a hierarchical causal model—can provide new opportunities for identification and estimation. As examples, we will study how to identify and estimate causal effects under unmeasured confounders, interference, and instruments.
(This is joint work with Eli Weinstein.)
David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. He studies probabilistic machine learning and Bayesian statistics, including theory, algorithms, and application. David has received several awards for his research. He received a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship (2017), and a Simons InvestigatorAward (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research. He is a fellow of the Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS).