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New research characterizes minimax risk for partial linear models

By PulseAugur Editorial · [1 sources] ·

Researchers have characterized the sharp structure-agnostic minimax risk for coefficient estimation in partial linear models. This work resolves an open problem in double machine learning by defining the available learner by approximation-error and stochastic-error budgets. The findings indicate that standard double machine learning may overstate the intrinsic difficulty of target estimation and suggest a principle for learner selection that balances approximation and stochastic complexity across nuisance learners. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New research characterizes minimax risk for partial linear models

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haichen Hu, David Simchi-Levi ·

    Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

    arXiv:2609.07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisances are learned by two distinct black-box learners, which resolves the open proble…

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