  Uncovering the heterogeneity of causal effects of policies and business
decisions at various levels of granularity provides substantial value to
decision makers. This paper develops new estimation and inference procedures
for multiple treatment models in a selection-on-observables framework by
modifying the Causal Forest approach suggested by Wager and Athey (2018) in
several dimensions. The new estimators have desirable theoretical,
computational and practical properties for various aggregation levels of the
causal effects. While an Empirical Monte Carlo study suggests that they
outperform previously suggested estimators, an application to the evaluation of
an active labour market programme shows the value of the new methods for
applied research.
