  Predictive models that generalize well under distributional shift are often
desirable and sometimes crucial to building robust and reliable machine
learning applications. We focus on distributional shift that arises in causal
inference from observational data and in unsupervised domain adaptation. We
pose both of these problems as prediction under a shift in design. Popular
methods for overcoming distributional shift make unrealistic assumptions such
as having a well-specified model or knowing the policy that gave rise to the
observed data. Other methods are hindered by their need for a pre-specified
metric for comparing observations, or by poor asymptotic properties. We devise
a bound on the generalization error under design shift, incorporating both
representation learning and sample re-weighting. Based on the bound, we propose
an algorithmic framework that does not require any of the above assumptions and
which is asymptotically consistent. We empirically study the new framework
using two synthetic datasets, and demonstrate its effectiveness compared to
previous methods.

Machine Learning