  Observational studies are rising in importance due to the widespread
accumulation of data in fields such as healthcare, education, employment and
ecology. We consider the task of answering counterfactual questions such as,
"Would this patient have lower blood sugar had she received a different
medication?". We propose a new algorithmic framework for counterfactual
inference which brings together ideas from domain adaptation and representation
learning. In addition to a theoretical justification, we perform an empirical
comparison with previous approaches to causal inference from observational
data. Our deep learning algorithm significantly outperforms the previous
state-of-the-art.
