You are all cordially invited to the UvA-Bosch Delta lab seminar on Thursday October 17th October at 15:00 on the Roeterseilandcampus A2.11 , where David Blei, well known for his fantastic work on LDA, Bayesian nonparametrics, and variational inference. He will give a talk on “The Blessings of Multiple Causes”.
Causal inference from observational data is a vital problem, but itcomes with strong assumptions. Most methods require that we observeall confounders, variables that affect both the causal variables andthe outcome variables. But whether we have observed all confounders isa famously untestable assumption. We describe the deconfounder, a wayto do causal inference with weaker assumptions than the classicalmethods require.
How does the deconfounder work? While traditional causal methodsmeasure the effect of a single cause on an outcome, many modernscientific studies involve multiple causes, different variables whoseeffects are simultaneously of interest. The deconfounder uses thecorrelation among multiple causes as evidence for unobservedconfounders, combining unsupervised machine learning and predictivemodel checking to perform causal inference. We demonstrate thedeconfounder on real-world data and simulation studies, and describethe theoretical requirements for the deconfounder to provide unbiasedcausal estimates.
This is joint work with Yixin Wang.
David Blei is a Professor of Statistics and Computer Science atColumbia University, and a member of the Columbia Data ScienceInstitute. He studies probabilistic machine learning, including itstheory, algorithms, and application. David has received several awardsfor his research, including a Sloan Fellowship (2010), Office of NavalResearch Young Investigator Award (2011), Presidential Early CareerAward for Scientists and Engineers (2011), Blavatnik Faculty Award(2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship(2017), and a Simons Investigator Award (2019). He is theco-editor-in-chief of the Journal of Machine Learning Research. He isa fellow of the ACM and the IMS.