Optimal Targeting in Dynamic Systems Article Swipe
Modern treatment targeting methods often rely on estimating the conditional average treatment effect (CATE) using machine learning tools. While effective in identifying who benefits from treatment on the individual level, these approaches typically overlook system-level dynamics that may arise when treatments induce strain on shared capacity. We study the problem of targeting in Markovian systems, where treatment decisions must be made one at a time as units arrive, and early decisions can impact later outcomes through delayed or limited access to resources. We show that optimal policies in such settings compare CATE-like quantities to state-specific thresholds, where each threshold reflects the expected cumulative impact on the system of treating an additional individual in the given state. We propose an algorithm that augments standard CATE estimation with off-policy evaluation techniques to estimate these thresholds from observational data. Theoretical results establish consistency and convergence guarantees, and empirical studies demonstrate that our method improves long-run outcomes considerably relative to individual-level CATE targeting rules.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2507.00312
- https://arxiv.org/pdf/2507.00312
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4416843135
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4416843135Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2507.00312Digital Object Identifier
- Title
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Optimal Targeting in Dynamic SystemsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-30Full publication date if available
- Authors
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Yu‐Chen HuList of authors in order
- Landing page
-
https://arxiv.org/abs/2507.00312Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2507.00312Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2507.00312Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
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