Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866915551204343808 |
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| author | Fernández-Loría, Carlos Loría, Jorge |
| author_facet | Fernández-Loría, Carlos Loría, Jorge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_12532 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization Fernández-Loría, Carlos Loría, Jorge Machine Learning 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. |
| title | Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2206.12532 |