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.

翻译：近年来，因果机器学习领域取得了显著进展。过去五年中引入的元学习器（arXiv:1706.03461v6）与异质双稳健估计器（arXiv:2004.14497）等方法代表了该领域的重要突破。然而，尽管存在这些进展，该领域仍面临挑战，特别是在处理紧密耦合系统时——此类系统中因果处理变量与混杂协变量需同时作为关键决策指标。这种场景常见于因果机器学习的营销应用，例如市场细分与增量营销提升。本研究提出经形式化证明的迭代因果分割算法以解决该问题。