  When a new treatment is considered for use, whether a pharmaceutical drug or
a search engine ranking algorithm, a typical question that arises is, will its
performance exceed that of the current treatment? The conventional way to
answer this counterfactual question is to estimate the effect of the new
treatment in comparison to that of the conventional treatment by running a
controlled, randomized experiment. While this approach theoretically ensures an
unbiased estimator, it suffers from several drawbacks, including the difficulty
in finding representative experimental populations as well as the cost of
running such trials. Moreover, such trials neglect the huge quantities of
available control-condition data which are often completely ignored.
  In this paper we propose a discriminative framework for estimating the
performance of a new treatment given a large dataset of the control condition
and data from a small (and possibly unrepresentative) randomized trial
comparing new and old treatments. Our objective, which requires minimal
assumptions on the treatments, models the relation between the outcomes of the
different conditions. This allows us to not only estimate mean effects but also
to generate individual predictions for examples outside the randomized sample.
  We demonstrate the utility of our approach through experiments in three
areas: Search engine operation, treatments to diabetes patients, and market
value estimation for houses. Our results demonstrate that our approach can
reduce the number and size of the currently performed randomized controlled
experiments, thus saving significant time, money and effort on the part of
practitioners.
