  For many kinds of interventions, such as a new advertisement, marketing
intervention, or feature recommendation, it is important to target a specific
subset of people for maximizing its benefits at minimum cost or potential harm.
However, a key challenge is that no data is available about the effect of such
a prospective intervention since it has not been deployed yet. In this work, we
propose a split-treatment analysis that ranks the individuals most likely to be
positively affected by a prospective intervention using past observational
data. Unlike standard causal inference methods, the split-treatment method does
not need any observations of the target treatments themselves. Instead it
relies on observations of a proxy treatment that is caused by the target
treatment. Under reasonable assumptions, we show that the ranking of
heterogeneous causal effect based on the proxy treatment is the same as the
ranking based on the target treatment's effect. In the absence of any
interventional data for cross-validation, Split-Treatment uses sensitivity
analyses for unobserved confounding to select model parameters. We apply
Split-Treatment to both a simulated data and a large-scale, real-world
targeting task and validate our discovered rankings via a randomized experiment
for the latter.
