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A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Semiparametric Off-Policy Inference for Optimal Policy Va...
[Submitted on 20 May 2025 (v1), last revised 25 Jun 2026 (this v · 2025-05-20 · via stat updates on arXiv.org

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Abstract:Off-policy evaluation (OPE) constructs confidence intervals for the value of a target policy using data generated under a different behavior policy. Most existing inference methods focus on fixed target policies and may fail when the target policy is estimated as optimal, particularly when the optimal policy is non-unique or nearly deterministic.
We study inference for the value of optimal policies in Markov decision processes. In an auxiliary augmented transition-sampling experiment, we characterize the existence of the efficient influence function and show that non-regularity arises when competing optimal policies havedistinct first-order gradients. For the actual i.i.d.-trajectory experiment, we derive the semiparametric efficiency bound and a uniformly weighted estimator that attains it under a unique optimum, while the sequential NSAVE procedure trades efficiency for stability and validity under non-uniqueness.
Motivated by this analysis, we propose a novel \textit{N}onparametric \textit{S}equenti\textit{A}l \textit{V}alue \textit{E}valuation (NSAVE) method, which yields martingale-based inference and retains a double-robustness property under policy-aligned nuisance estimation. We further develop a pointwise smoothing-based approximation under explicit first-stage rates, and a post-selection template with uniform coverage whenever its stated joint calibration condition is satisfied.
Simulation studies support the theoretical results. An application to the Drink Less micro-randomized trial provides confidence intervals for state-adaptive notification policies and their improvement over the randomized behavior policy.

Submission history

From: Haoyu Wei [view email]
[v1] Tue, 20 May 2025 01:41:44 UTC (435 KB)
[v2] Wed, 21 May 2025 23:42:11 UTC (435 KB)
[v3] Sun, 8 Jun 2025 20:39:30 UTC (409 KB)
[v4] Sun, 18 Jan 2026 19:11:12 UTC (246 KB)
[v5] Thu, 25 Jun 2026 18:11:27 UTC (465 KB)