The Blessings of Multiple Causes
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, pp. 1574.0-1596.0, 2019.
Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. This assumption is standard but it is also untestable. In this article, we develop the deconfounder, a way to do ...More
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