Summary of Intope: Off-policy Evaluation in the Presence Of Interference, by Yuqi Bai et al.
IntOPE: Off-Policy Evaluation in the Presence of Interference
by Yuqi Bai, Ziyu Zhao, Minqin Zhu, Kun Kuang
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to Off-Policy Evaluation (OPE) in real-world scenarios, where traditional methods relying on the Stable Unit Treatment Value Assumption (SUTVA) often fail due to interference. The authors introduce IntIPW, an Inverse Probability Weighting (IPW)-style estimator that integrates marginalized importance weights to account for both individual actions and peer influence. This approach is demonstrated to be effective in both synthetic and real-world data experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in using data from the past to predict how well a new idea will work. It’s called Off-Policy Evaluation, or OPE. Usually, people assume that what one person does doesn’t affect others. But in many cases, like with medicines and recommendations, this isn’t true. The authors created a new way to calculate how well something will work by considering both the individual’s actions and how those around them are behaving. This helps solve a big limitation of old methods. |
Keywords
» Artificial intelligence » Probability
