  Inferring individualised treatment effects from observational data can unlock
the potential for targeted interventions. It is, however, hard to infer these
effects from observational data. One major problem that can arise is covariate
shift where the data (outcome) conditional distribution remains the same but
the covariate (input) distribution changes between the training and test set.
In an observational data setting, this problem is materialised in control and
treated units coming from different distributions. A common solution is to
augment learning methods through reweighing schemes (e.g. propensity scores).
These are needed due to model misspecification, but might hurt performance in
the individual case. In this paper, we explore a novel generative tree based
approach that tackles model misspecification directly, helping downstream
estimators achieve better robustness. We show empirically that the choice of
model class can indeed significantly affect the final performance and that
reweighing methods can struggle in individualised effect estimation. Our
proposed approach is competitive with reweighing methods on average treatment
effects while performing significantly better on individualised treatment
effects.
