Causal Machine Learning for Moderation Effects

arXiv:2401.08290v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv causal challenge decision decisions differences econ.em effects gate impact literature machine machine learning maker moderation paper stat.ml subgroups tools treatment type