# Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization

arXiv: https://arxiv.org/abs/2206.12532 · HTML: https://mlsanity.com/paper/2206.12532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters](https://mlsanity.com/paper/2104.04103/) (2021)
- [Targeting for long-term outcomes](https://mlsanity.com/paper/2010.15835/) (2020)
- [Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index](https://mlsanity.com/paper/1603.09326/) (2016)
- [A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation](https://mlsanity.com/paper/2004.11532/) (2020)

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

- [Metalearners for Ranking Treatment Effects](https://mlsanity.com/paper/2405.02183/) (2024)

## Abstract (arXiv)

Who should we prioritize for treatment when causal effects cannot be estimated? In practice, organizations often rely on predictive proxies: ads are targeted using purchase probabilities, and retention incentives are allocated using churn-risk scores. These models are not causal, but they are often used with the aim of ranking individuals by treatment effects, a task we call causal ordering. We develop a decision-focused framework to reason about this practice. We identify conditions under which proxies recover the correct effect ordering, which hold when a proxy reflects a dominant moderator of treatment effects. We show how these conditions emerge as a useful approximation in discrete choice settings, where the propensity to act without an intervention moderates persuasion. Moreover, we extend beyond this case, demonstrating that proxies capturing a non-dominant moderator can still outperform CATE estimates when they target signals that are easier to estimate precisely. Building on these insights, we introduce diagnostic tools to assess proxy usefulness in practice. Finally, we illustrate the framework in advertising, where a simple predictive proxy outperforms heterogeneous-effect estimation methods.