Summary of Fair Risk Minimization Under Causal Path-specific Effect Constraints, by Razieh Nabi et al.
Fair Risk Minimization under Causal Path-Specific Effect Constraints
by Razieh Nabi, David Benkeser
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 framework for estimating fair optimal predictions in machine learning settings where fairness can be quantified using path-specific causal effects. The approach utilizes Lagrange multipliers for infinite-dimensional functional estimation to derive closed-form solutions for constrained optimization based on mean squared error and cross-entropy risk criteria. The theoretical forms of the solutions highlight nuanced adjustments to the unconstrained minimizer, showcasing trade-offs between risk minimization and achieving fairness. Theoretical solutions serve as the basis for constructing flexible semiparametric estimation strategies for nuisance components. The paper also explores robustness properties of estimators in terms of achieving optimal constrained risk and controlling constraint values. Simulation studies validate the theory’s impact on using robust estimators of pathway-specific effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make fair predictions with machine learning models. It introduces a new way to balance two important goals: minimizing mistakes (risk) and being fair. The approach uses special mathematical tools called Lagrange multipliers to find solutions that meet these goals. The results show how making one adjustment can help achieve fairness, but also highlight the trade-offs involved. The paper’s ideas can be used in real-world applications to create more fair models. |
Keywords
» Artificial intelligence » Cross entropy » Machine learning » Optimization
