  Uncovering causal effects at various levels of granularity provides
substantial value to decision makers. Comprehensive machine learning approaches
to causal effect estimation allow to use a single causal machine learning
approach for estimation and inference of causal mean effects for all levels of
granularity. Focusing on selection-on-observables, this paper compares three
such approaches, the modified causal forest (mcf), the generalized random
forest (grf), and double machine learning (dml). It also provides proven
theoretical guarantees for the mcf and compares the theoretical properties of
the approaches. The findings indicate that dml-based methods excel for average
treatment effects at the population level (ATE) and group level (GATE) with few
groups, when selection into treatment is not too strong. However, for finer
causal heterogeneity, explicitly outcome-centred forest-based approaches are
superior. The mcf has three additional benefits: (i) It is the most robust
estimator in cases when dml-based approaches underperform because of
substantial selectivity; (ii) it is the best estimator for GATEs when the
number of groups gets larger; and (iii), it is the only estimator that is
internally consistent, in the sense that low-dimensional causal ATEs and GATEs
are obtained as aggregates of finer-grained causal parameters.
