The Blessings of Multiple Causes

  • David Blei | Columbia University

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.

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Speaker Bios

David Blei is a Professor of Statistics and Computer Science at Columbia University. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David earned his Bachelor’s degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from the University of California, Berkeley (2004). Before arriving to Columbia, he was an Associate Professor of Computer Science at Princeton University. He 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), and ACM-Infosys Foundation Award (2013).

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