You are here

Seminar on Theoretical Machine Learning

The Blessings of Multiple Causes

Causal inference from observational data is a vital problem, but it
comes with strong assumptions. Most methods require that we observe
all confounders, variables that affect both the causal variables and
the outcome variables. But whether we have observed all confounders is
a famously untestable assumption. We describe the deconfounder, a way
to do causal inference with weaker assumptions than the classical
methods require.

How does the deconfounder work? While traditional causal methods
measure the effect of a single cause on an outcome, many modern
scientific studies involve multiple causes, different variables whose
effects are simultaneously of interest. The deconfounder uses the
correlation among multiple causes as evidence for unobserved
confounders, combining unsupervised machine learning and predictive
model checking to perform causal inference. We demonstrate the
deconfounder on real-world data and simulation studies, and describe
the theoretical requirements for the deconfounder to provide unbiased
causal estimates.

This is joint work with Yixin Wang.


David M. Blei

Speaker Affiliation

Columbia University



Event Series




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, including its theory, algorithms, and application. David has received several awards for his research, including 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 Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research.  He is a fellow of the ACM and the IMS.

Additional Information

Date & Time
January 21, 2020 | 12:001:30pm


Dilworth Room