dorsal/arxiv
View SchemaGood Allocations from Bad Estimates
| Authors | Sílvia Casacuberta, Moritz Hardt |
|---|---|
| Categories | |
| ArXiv ID | 2601.05597vv1 |
| URL | https://arxiv.org/abs/2601.05597 |
| 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 $\epsilon > 0$ 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 $O(M/\epsilon^2)$ samples. This is best possible if the goal is to estimate all treatment effects up to an $\epsilon$ error. In this work, we show how to achieve the same total treatment effect as CATE with only $O(M/\epsilon)$ 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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"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 $\\epsilon \u003e 0$ 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 $O(M/\\epsilon^2)$ samples. This is best possible if the goal is to estimate all treatment effects up to an $\\epsilon$ error. In this work, we show how to achieve the same total treatment effect as CATE with only $O(M/\\epsilon)$ 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.",
"arxiv_id": "2601.05597",
"authors": [
"S\u00edlvia Casacuberta",
"Moritz Hardt"
],
"categories": [
"cs.LG",
"cs.CY"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Good Allocations from Bad Estimates",
"url": "https://arxiv.org/abs/2601.05597",
"version": "v1"
},
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