  In this tech report we discuss the evaluation problem of contextual uplift
modeling from the causal inference point of view. More particularly, we
instantiate the individual treatment effect (ITE) estimation, and its
evaluation counterpart. First, we unify two well studied fields: the
statistical ITE approach and its observational counterpart based on uplift
study. Then we exhibit the problem of evaluation, based on previous work about
ITE and uplift modeling. Moreover, we derive a new estimator for the uplift
curve, built on logged bandit feedback dataset, that reduces its variance. We
prove that AUUC fails on non randomized control trial (RCT) datasets and
discuss some corrections and guidelines that should be kept in mind while using
AUUC (re-balancing the population, local importance sampling using propensity
score).
