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The Tea Talk Team
TITLE The Blessings of Multiple Causes
ABSTRACT
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 correlate to both the causal variables
(the treatment) and the effect of those variables (how well the
treatment works). But whether we have observed all confounders is a
famously untestable assumption. We describe the deconfounder, a way
to do causal inference from observational data with weaker assumptions
that 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
multiple causes as a signal for unobserved confounders, combining
unsupervised machine learning and predictive model checking to perform
causal inference.
We describe the theoretical requirements for the deconfounder to
provide unbiased causal estimates, and show that it requires weaker
assumptions than classical causal inference. We analyze the
deconfounder's performance in three types of studies: semi-simulated
data around smoking and lung cancer, semi-simulated data around
genomewide association studies, and a real dataset about actors and
movie revenue. The deconfounder provides a checkable approach to
estimating close-to-truth causal effects.
This is joint work with Yixin Wang.
[*]
https://arxiv.org/abs/1805.06826
BIO
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), and a Guggenheim fellowship (2017). He is the co-editor-in-chief of the Journal of Machine Learning Research. He is a fellow of the ACM and the IMS. He is a fellow of the ACM and the IMS.