Statistical Inference for Misspecified Contextual Bandits
Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment. Yet these advantages create challenges for statistical inference due to adaptivity. We study inference with contextual-bandit data without assuming a well-specified outcome model. In this setting, we show a previously overlooked issue: standard algorithms such as LinUCB may fail to stabilize under misspecified working models, leading to non-Gaussian estimator behavior and invalid inference. This issue is practically important, as misspecified working models -- such as approximations of complex dynamical systems -- are often employed by online agents in real-wor...
| Authors | Yongyi Guo, Ziping Xu |
|---|---|
| Primary category | stat.ML |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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