Good Allocations from Bad Estimates

• URL: http://arxiv.org/abs/2601.05597v1
• Date: Fri, 09 Jan 2026 07:35:50 GMT
• Title: Good Allocations from Bad Estimates
• Authors: Sílvia Casacuberta, Moritz Hardt,
• Abstract summary: Conditional average treatment effect estimation is the de facto gold standard for targeting a treatment to a heterogeneous population.<n>We show how to achieve the same total treatment effect with only $O(M/)$ samples for natural distributions of treatment effects.
• Score: 32.89611771415222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conditional average treatment effect (CATE) estimation is the de facto gold standard for targeting a treatment to a heterogeneous population. The method estimates treatment effects up to an error $ε> 0$ in each of $M$ different strata of the population, targeting individuals in decreasing order of estimated treatment effect until the budget runs out. In general, this method requires $O(M/ε^2)$ samples. This is best possible if the goal is to estimate all treatment effects up to an $ε$ error. In this work, we show how to achieve the same total treatment effect as CATE with only $O(M/ε)$ samples for natural distributions of treatment effects. The key insight is that coarse estimates suffice for near-optimal treatment allocations. In addition, we show that budget flexibility can further reduce the sample complexity of allocation. Finally, we evaluate our algorithm on various real-world RCT datasets. In all cases, it finds nearly optimal treatment allocations with surprisingly few samples. Our work highlights the fundamental distinction between treatment effect estimation and treatment allocation: the latter requires far fewer samples.

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