  When doing impact evaluation and making causal inferences, it is important to
acknowledge the heterogeneity of the treatment effects for different domains
(geographic, socio-demographic, or socio-economic). If the domain of interest
is small with regards to its sample size (or even zero in some cases), then the
evaluator has entered the small area estimation (SAE) dilemma.
  Based on the modification of the Inverse Propensity Weighting estimator and
the traditional small area predictors, the paper proposes a new methodology to
estimate area specific average treatment effects for unplanned domains. By
means of these methods we can also provide a map of policy impacts, that can
help to better target the treatment group(s). We develop analytical Mean
Squared Error (MSE) estimators of the proposed predictors. An extensive
simulation analysis, also based on real data, shows that the proposed
techniques in most cases lead to more efficient estimators.
