Summary of Best Of Three Worlds: Adaptive Experimentation For Digital Marketing in Practice, by Tanner Fiez et al.
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
by Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 investigates the challenges of applying adaptive experimental design (AED) methods to real-world industrial settings where non-stationarity is prevalent. Unlike traditional A/B/N testing methods, AED aims to boost testing throughput or reduce experimentation cost. However, the guarantees and behavior of AED systems are not well-understood in such environments. The authors share their experiences and provide perspectives on the proper objectives and system specifications for using AED in industrial settings. They also propose an AED framework for counterfactual inference based on these lessons learned and test it in a commercial environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AED methods are used in industry to speed up testing or reduce costs, but they don’t work well in real-world situations where things change. The authors looked at what happens when you try to use AED in these situations and found that it’s not as straightforward as they thought. They share their findings and suggest ways to make AED work better in industrial settings. |
Keywords
* Artificial intelligence * Inference
