Summary of Causal Deepsets For Off-policy Evaluation Under Spatial or Spatio-temporal Interferences, by Runpeng Dai et al.
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences
by Runpeng Dai, Jianing Wang, Fan Zhou, Shikai Luo, Zhiwei Qin, Chengchun Shi, Hongtu Zhu
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper introduces a causal deepset framework for off-policy evaluation (OPE), which relaxes mean-field assumptions prevalent in existing OPE methodologies. The proposed approach incorporates permutation invariance (PI) assumption, enabling adaptive learning of the mean-field function and more flexible estimation methods. Novel algorithms are presented, demonstrating significant precision improvements over baseline methods. This work has practical implications for evaluating novel products or policies from offline datasets in sectors like pharmaceuticals and e-commerce. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-policy evaluation is important for evaluating the effectiveness of new products or policies. A new approach called causal deepset framework relaxes some assumptions that are not suitable for real-world situations. The approach uses permutation invariance to learn how to estimate better. This method can be used to evaluate things like new medicines or e-commerce strategies. |
Keywords
* Artificial intelligence * Precision
