  Causal inference from observational data often assumes "ignorability," that
all confounders are observed. This assumption is standard yet untestable.
However, many scientific studies involve multiple causes, different variables
whose effects are simultaneously of interest. We propose the deconfounder, an
algorithm that combines unsupervised machine learning and predictive model
checking to perform causal inference in multiple-cause settings. The
deconfounder infers a latent variable as a substitute for unobserved
confounders and then uses that substitute to perform causal inference. We
develop theory for the deconfounder, and show that it requires weaker
assumptions than classical causal inference. We analyze its performance in
three types of studies: semi-simulated data around smoking and lung cancer,
semi-simulated data around genome-wide association studies, and a real dataset
about actors and movie revenue. The deconfounder provides a checkable approach
to estimating closer-to-truth causal effects.
