# A framework for causal segmentation analysis with machine learning in large-scale digital experiments

arXiv: https://arxiv.org/abs/2111.01223 · HTML: https://mlsanity.com/paper/2111.01223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [A Unifying Framework for Parallel and Distributed Processing in R using Futures](https://mlsanity.com/paper/2008.00553/) (2020)
- [Evaluating the Impact of Treating the Optimal Subgroup](https://mlsanity.com/paper/1603.06372/) (2016)
- [Selecting optimal subgroups for treatment using many covariates](https://mlsanity.com/paper/1802.09642/) (2018)

## Built on by (most cited first, 1 of 1)

- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)

## Abstract (arXiv)

We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digital experiments. Building on recent developments in causal inference and non/semi-parametric statistics, our approach unifies two objectives: (1) the discovery of user segments that stand to benefit from a candidate treatment based on subgroup-specific treatment effects, and (2) the evaluation of causal impacts of dynamically assigning units to a study's treatment arm based on their predicted segment-specific benefit or harm. Our proposal is model-agnostic, capable of incorporating state-of-the-art machine learning algorithms into the estimation procedure, and is applicable in randomized A/B tests and quasi-experiments. An open source R package implementation, sherlock, is introduced.