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IntOPE: Off-Policy Evaluation in the Presence of Interference

arXiv 2408.13484 ↗First posted 24 Aug 2024cs.LG cs.IRcited by 0

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Abstract (arXiv)

Off-Policy Evaluation (OPE) is employed to assess the potential impact of a hypothetical policy using logged contextual bandit feedback, which is crucial in areas such as personalized medicine and recommender systems, where online interactions are associated with significant risks and costs. Traditionally, OPE methods rely on the Stable Unit Treatment Value Assumption (SUTVA), which assumes that the reward for any given individual is unaffected by the actions of others. However, this assumption often fails in real-world scenarios due to the presence of interference, where an individual's reward is affected not just by their own actions but also by the actions of their peers. This realization reveals significant limitations of existing OPE methods in real-world applications. To address this limitation, we propose IntIPW, an IPW-style estimator that extends the Inverse Probability Weighting (IPW) framework by integrating marginalized importance weights to account for both individual actions and the influence of adjacent entities. Extensive experiments are conducted on both synthetic and real-world data to demonstrate the effectiveness of the proposed IntIPW method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 24 Aug 2024.

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References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2020Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluationcited by 77
  3. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  4. 2020Off-policy Bandits with Deficient Supportcited by 60
  5. 2018Policy Evaluation and Optimization with Continuous Treatmentscited by 57
  6. 2016SNAP: A General Purpose Network Analysis and Graph Mining Librarycited by 56
  7. 2019Learning Individual Causal Effects from Networked Observational Datacited by 56
  8. 2022Off-Policy Evaluation for Large Action Spaces via Embeddingscited by 53
  9. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  10. 2020Causal Inference under Networked Interference and Intervention Policy Enhancementcited by 37
  11. 2022Learning Causal Effects on Hypergraphscited by 31
  12. 2017Causal inference for social network datacited by 30
6 more
  1. 2019Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learningcited by 28
  2. 2017Auto-G-Computation of Causal Effects on a Networkcited by 16
  3. 2020Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulationcited by 13
  4. 2019Balanced off-policy evaluation in general action spacescited by 11
  5. 2022Learning Individual Treatment Effects under Heterogeneous Interference in Networkscited by 11
  6. 2023Individualized Policy Evaluation and Learning under Clustered Network Interferencecited by 6

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