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

TL;DR

This paper presents a framework for using predictive proxies to prioritize treatments without estimating causal effects, identifying conditions where proxies accurately reflect effect orderings and providing tools to evaluate their usefulness.

Contribution

It introduces a decision-focused framework for causal ordering using proxies, including conditions for their effectiveness and diagnostic tools, with practical illustration in advertising.

Findings

Proxies reflecting dominant moderators can recover correct effect orderings.

Non-dominant moderator proxies can outperform CATE estimates when easier to estimate.

A simple proxy can outperform complex effect estimation methods in advertising.

Abstract

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.