# Shrinkage-Based Regressions with Many Related Treatments

arXiv: https://arxiv.org/abs/2507.01202 · HTML: https://mlsanity.com/paper/2507.01202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Double/Debiased Machine Learning for Treatment and Causal Parameters](https://mlsanity.com/paper/1608.00060/) (2016)
- [Applied Causal Inference Powered by ML and AI](https://mlsanity.com/paper/2403.02467/) (2024)
- [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.01939/) (2022)
- [Identification of Average Treatment Effects in Nonparametric Panel Models](https://mlsanity.com/paper/2503.19873/) (2025)
- [Does Regression Produce Representative Causal Rankings?](https://mlsanity.com/paper/2411.02675/) (2024)

## Abstract (arXiv)

When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.