Summary of Federated Learning For Estimating Heterogeneous Treatment Effects, by Disha Makhija et al.
Federated Learning for Estimating Heterogeneous Treatment Effects
by Disha Makhija, Joydeep Ghosh, Yejin Kim
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers develop a novel framework for estimating heterogeneous treatment effects (HTE) using Federated Learning. The proposed approach enables collaborative learning across institutions, addressing the challenge of collecting large amounts of data per treatment. By jointly learning a common feature representation and concurrently learning predictive functions for outcomes under distinct interventions, the authors demonstrate that even with diverse interventions and subject populations, personalized decision-making can be achieved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning methods help make better decisions by understanding how different treatments affect people in different ways. Currently, these methods need lots of data for each treatment, which is hard to collect because it’s expensive to run many experiments. To solve this problem, researchers created a new way to learn about treatment effects using something called Federated Learning. This method lets multiple institutions work together to learn how treatments affect people without sharing all their data. The authors developed an algorithm that uses special kinds of transformers to help machines understand patterns in the data and make good decisions. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
