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Yang Xu 0089

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3 papers

1 author row

ICML Conference 2025 Conference Paper

Linear Contextual Bandits With Interference

Yang Xu 0089
Wenbin Lu
Rui Song 0006

Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit’s actions on the rewards of others. In contextual bandit (CB) settings where multiple units are present in the same round, interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in interference-aware settings, how to model interference in CB remains significantly underexplored. In this paper, we introduce a systematic framework to address interference in Linear CB (LinCB), bridging the gap between causal inference and online decision-making. We propose a series of algorithms that explicitly quantify the interference effect in the reward modeling process and provide comprehensive theoretical guarantees, including sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The effectiveness of our approach is demonstrated through simulations and a synthetic data generated based on MovieLens data.

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ICML Conference 2025 Conference Paper

Off-Policy Evaluation under Nonignorable Missing Data

Han Wang
Yang Xu 0089
Wenbin Lu
Rui Song 0006

Off-Policy Evaluation (OPE) aims to estimate the value of a target policy using offline data collected from potentially different policies. In real-world applications, however, logged data often suffers from missingness. While OPE has been extensively studied in the literature, a theoretical understanding of how missing data affects OPE results remains unclear. In this paper, we investigate OPE in the presence of monotone missingness and theoretically demonstrate that the value estimates remain unbiased under ignorable missingness but can be biased under nonignorable (informative) missingness. To retain the consistency of value estimation, we propose an inverse probability weighting value estimator and conduct statistical inference to quantify the uncertainty of the estimates. Through a series of numerical experiments, we empirically demonstrate that our proposed estimator yields a more reliable value inference under missing data.

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ICML Conference 2023 Conference Paper

An Instrumental Variable Approach to Confounded Off-Policy Evaluation

Yang Xu 0089
Jin Zhu
Chengchun Shi
Shikai Luo
Rui Song 0006

Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In many cases, there exist unmeasured variables that confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded sequential decision making. Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy’s value in infinite horizon settings as well. Furthermore, we propose a number of policy value estimators and illustrate their effectiveness through extensive simulations and real data analysis from a world-leading short-video platform.

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