  It is valuable for any decision maker to know the impact of decisions
(treatments) on average and for subgroups. The causal machine learning
literature has recently provided tools for estimating group average treatment
effects (GATE) to understand treatment heterogeneity better. This paper
addresses the challenge of interpreting such differences in treatment effects
between groups while accounting for variations in other covariates. We propose
a new parameter, the balanced group average treatment effect (BGATE), which
measures a GATE with a specific distribution of a priori-determined covariates.
By taking the difference of two BGATEs, we can analyse heterogeneity more
meaningfully than by comparing two GATEs. The estimation strategy for this
parameter is based on double/debiased machine learning for discrete treatments
in an unconfoundedness setting, and the estimator is shown to be
$\sqrt{N}$-consistent and asymptotically normal under standard conditions.
Adding additional identifying assumptions allows specific balanced differences
in treatment effects between groups to be interpreted causally, leading to the
causal balanced group average treatment effect. We explore the finite sample
properties in a small-scale simulation study and demonstrate the usefulness of
these parameters in an empirical example.
