  Estimating causal effects in e-commerce tends to involve costly treatment
assignments which can be impractical in large-scale settings. Leveraging
machine learning to predict such treatment effects without actual intervention
is a standard practice to diminish the risk. However, existing methods for
treatment effect prediction tend to rely on training sets of substantial size,
which are built from real experiments and are thus inherently risky to create.
In this work we propose a graph neural network to diminish the required
training set size, relying on graphs that are common in e-commerce data.
Specifically, we view the problem as node regression with a restricted number
of labeled instances, develop a two-model neural architecture akin to previous
causal effect estimators, and test varying message-passing layers for encoding.
Furthermore, as an extra step, we combine the model with an acquisition
function to guide the creation of the training set in settings with extremely
low experimental budget. The framework is flexible since each step can be used
separately with other models or treatment policies. The experiments on real
large-scale networks indicate a clear advantage of our methodology over the
state of the art, which in many cases performs close to random, underlining the
need for models that can generalize with limited supervision to reduce
experimental risks.
