  Applying causal inference models in areas such as economics, healthcare and
marketing receives great interest from the machine learning community. In
particular, estimating the individual-treatment-effect (ITE) in settings such
as precision medicine and targeted advertising has peaked in application.
Optimising this ITE under the strong-ignorability-assumption -- meaning all
confounders expressing influence on the outcome of a treatment are registered
in the data -- is often referred to as uplift modeling (UM). While these
techniques have proven useful in many settings, they suffer vividly in a
dynamic environment due to concept drift. Take for example the negative
influence on a marketing campaign when a competitor product is released. To
counter this, we propose the uplifted contextual multi-armed bandit (U-CMAB), a
novel approach to optimise the ITE by drawing upon bandit literature.
Experiments on real and simulated data indicate that our proposed approach
compares favourably against the state-of-the-art. All our code can be found
online at https://github.com/vub-dl/u-cmab.
