Summary of Metalearners For Ranking Treatment Effects, by Toon Vanderschueren et al.
Metalearners for Ranking Treatment Effects
by Toon Vanderschueren, Wouter Verbeke, Felipe Moraes, Hugo Manuel Proença
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposed methodology learns an allocation policy by prioritizing instances based on their incremental profit, considering budget constraints. The approach is built upon learning to rank, which directly optimizes the ranking model using efficient sampling procedures for large-scale datasets. This methodology outperforms existing methods in uplift modeling and causal inference, which primarily estimate treatment effects without considering operational context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of allocating treatments with a budget constraint. Instead of just estimating how well something will work, it tries to find the best allocation of resources to get the most profit. This is done by learning to rank, or prioritize, different options based on their potential return. The method is tested on both fake and real data and shown to be effective. |
Keywords
» Artificial intelligence » Inference
