Summary of Graph Machine Learning Based Doubly Robust Estimator For Network Causal Effects, by Seyedeh Baharan Khatami et al.
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
by Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, Sudeepa Roy, Babak Salimi
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI); 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 proposed methodology combines graph machine learning approaches with double machine learning framework to estimate direct and peer effects in social networks without prior assumptions about network-induced confounding mechanisms. The novel approach is semiparametric efficient, allowing for consistent uncertainty quantification. It is demonstrated to be accurate, robust, and scalable via an extensive simulation study. Additionally, the method is applied to investigate the impact of Self-Help Group participation on financial risk tolerance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out how people’s behavior in social networks affects each other. This can be tricky because it’s hard to tell what would have happened if someone didn’t participate in a group or activity. The authors come up with a new way of using machine learning and statistical techniques to solve this problem without making assumptions about the relationships between people in the network. They test their method on fake data and show that it works well, then apply it to a real-life example looking at how participation in self-help groups affects financial risk-taking. |
Keywords
* Artificial intelligence * Machine learning
