Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy
This work addresses a specific problem in marketing applications like segmentation and uplift modeling, but appears incremental as it builds on existing causal ML methods.
The paper tackles the challenge of managing tightly coupled systems in causal machine learning for marketing, where both treatment variables and confounding covariates are key decision indicators, by introducing an iterative causal segmentation algorithm.
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.