Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations

• URL: http://arxiv.org/abs/2212.14411v5
• Date: Mon, 11 Mar 2024 17:33:13 GMT
• Title: Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
• Authors: Aurelien Bibaut, Nathan Kallus, Michael Lindon
• Abstract summary: We provide the first type-I-error and expected-rejection-time guarantees under general non-data generating processes. We show how to apply our results to inference on parameters defined by estimating equations, such as average treatment effects.
• Score: 44.71254888821376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential tests and their implied confidence sequences, which are valid at arbitrary stopping times, promise flexible statistical inference and on-the-fly decision making. However, strong guarantees are limited to parametric sequential tests that under-cover in practice or concentration-bound-based sequences that over-cover and have suboptimal rejection times. In this work, we consider classic delayed-start normal-mixture sequential probability ratio tests, and we provide the first asymptotic type-I-error and expected-rejection-time guarantees under general non-parametric data generating processes, where the asymptotics are indexed by the test's burn-in time. The type-I-error results primarily leverage a martingale strong invariance principle and establish that these tests (and their implied confidence sequences) have type-I error rates asymptotically equivalent to the desired (possibly varying) $\alpha$-level. The expected-rejection-time results primarily leverage an identity inspired by It\^o's lemma and imply that, in certain asymptotic regimes, the expected rejection time is asymptotically equivalent to the minimum possible among $\alpha$-level tests. We show how to apply our results to sequential inference on parameters defined by estimating equations, such as average treatment effects. Together, our results establish these (ostensibly parametric) tests as general-purpose, non-parametric, and near-optimal. We illustrate this via numerical simulations and a real-data application to A/B testing at Netflix.

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