  We present a new machine learning approach to estimate personalized treatment
effects in the classical potential outcomes framework with binary outcomes. To
overcome the problem that both treatment and control outcomes for the same unit
are required for supervised learning, we propose surrogate loss functions that
incorporate both treatment and control data. The new surrogates yield tighter
bounds than the sum of losses for treatment and control groups. A specific
choice of loss function, namely a type of hinge loss, yields a minimax support
vector machine formulation. The resulting optimization problem requires the
solution to only a single convex optimization problem, incorporating both
treatment and control units, and it enables the kernel trick to be used to
handle nonlinear (also non-parametric) estimation. Statistical learning bounds
are also presented for the framework, and experimental results.
