Summary of Sequential Conditional Transport on Probabilistic Graphs For Interpretable Counterfactual Fairness, by Agathe Fernandes Machado et al.
Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
by Agathe Fernandes Machado, Arthur Charpentier, Ewen Gallic
First submitted to arxiv on: 6 Aug 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 proposes a novel method for deriving counterfactuals by combining two existing approaches: adaptations based on causal graphs and optimal transport. The authors extend these techniques to probabilistic graphical models, creating a new framework called sequential transport. This approach is then applied to the problem of fairness at the individual level, using numerical experiments on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper links two existing methods to create counterfactuals that make sense for fairness at an individual level. It takes ideas from causal graphs and transportation theory to develop a new way called sequential transport. The authors test this approach on fake and real data sets, showing it works well. |
