  The field of causal Machine Learning (ML) has made significant strides in
recent years. Notable breakthroughs include methods such as meta learners
(arXiv:1706.03461v6) and heterogeneous doubly robust estimators
(arXiv:2004.14497) introduced in the last five years. Despite these
advancements, the field still faces challenges, particularly in managing
tightly coupled systems where both the causal treatment variable and a
confounding covariate must serve as key decision-making indicators. This
scenario is common in applications of causal ML for marketing, such as
marketing segmentation and incremental marketing uplift. In this work, we
present our formally proven algorithm, iterative causal segmentation, to
address this issue.
