Summary of Online Experimental Design with Estimation-regret Trade-off Under Network Interference, by Zhiheng Zhang et al.
Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
by Zhiheng Zhang, Zichen Wang
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Statistics Theory (math.ST)
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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 A unified framework for online experimental design is proposed to estimate interference-aware causal effects in networked environments. Traditional methods assume independent treatment effects among individuals, which may not hold in these settings. To address this issue, the authors introduce a novel approach that utilizes exposure mapping to represent treatment effects in a more flexible and context-aware manner. The framework achieves a Pareto-optimal trade-off between estimation accuracy and regret under different network topologies and time periods. Algorithmic implementation is also discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle the challenge of estimating causal effects in networks where one person’s treatment can affect others. They introduce a new way to design online experiments that takes into account these network effects. This approach uses a concept called exposure mapping to represent how treatments affect different people in the network. The results show that this method is better than previous methods at balancing accuracy and regret. |
