  Estimating a unit's responses to interventions with an associated dose, the
"conditional average dose response" (CADR), is relevant in a variety of
domains, from healthcare to business, economics, and beyond. Such a response
typically needs to be estimated from observational data, which introduces
several challenges. That is why the machine learning (ML) community has
proposed several tailored CADR estimators. Yet, the proposal of most of these
methods requires strong assumptions on the distribution of data and the
assignment of interventions, which go beyond the standard assumptions in causal
inference. Whereas previous works have so far focused on smooth shifts in
covariate distributions across doses, in this work, we will study estimating
CADR from clustered data and where different doses are assigned to different
segments of a population. On a novel benchmarking dataset, we show the impacts
of clustered data on model performance and propose an estimator, CBRNet, that
learns cluster-agnostic and hence dose-agnostic covariate representations
through representation balancing for unbiased CADR inference. We run extensive
experiments to illustrate the workings of our method and compare it with the
state of the art in ML for CADR estimation.
