Summary of Meta-learners For Partially-identified Treatment Effects Across Multiple Environments, by Jonas Schweisthal et al.
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
by Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a new approach to estimating the conditional average treatment effect (CATE) from observational data in complex environments, where data come from multiple settings such as different hospitals or countries. It relaxes standard causal assumptions and focuses on partial identification of CATE using instrumental variables. The authors develop model-agnostic learners, or meta-learners, that can be used with any machine learning model to estimate the bounds of CATE. They demonstrate the effectiveness of their approach using both simulated and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to use data from different places to figure out what works best for each person. It’s like trying to find the best medicine for someone, but you have data from many hospitals or doctors. The researchers came up with a new way to look at this problem by using something called an “instrumental variable”. This lets them get close to the right answer even if some things don’t perfectly match up. They also created special tools that can be used with any kind of machine learning model to help estimate what will work best for each person. |
Keywords
» Artificial intelligence » Machine learning
