# Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy

arXiv: https://arxiv.org/abs/2405.14743 · HTML: https://mlsanity.com/paper/2405.14743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 3 of 3)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)

## Built on by (most cited first, 2 of 2)

- [When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals](https://mlsanity.com/paper/2607.08065/) (2026)
- [Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study](https://mlsanity.com/paper/2607.21866/) (2026)

## Abstract (arXiv)

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.