Fair Risk Minimization under Causal Path-Specific Effect Constraints

TL;DR

This paper develops a method for fair machine learning predictions by incorporating causal path-specific effects into the optimization process, balancing fairness and accuracy with theoretical and practical insights.

Contribution

It introduces a novel framework using Lagrange multipliers for causal fairness constraints, providing closed-form solutions and flexible estimation strategies.

Findings

Theoretical analysis of fairness-risk trade-offs.

Robust semiparametric estimators for causal effects.

Simulation results validating the approach.

Abstract

This paper introduces a framework for estimating fair optimal predictions using machine learning where the notion of fairness can be quantified using path-specific causal effects. We use a recently developed approach based on 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 are analyzed in detail and described as nuanced adjustments to the unconstrained minimizer. This analysis highlights important trade-offs between risk minimization and achieving fairnes. The theoretical solutions are also used as the basis for construction of flexible semiparametric estimation strategies for these nuisance components. We describe the robustness properties of our estimators in terms of achieving the optimal constrained risk, as well as in terms of controlling the value of the constraint. We study via simulation the impact of using robust estimators of pathway-specific effects to validate our theory. This work advances the discourse on algorithmic fairness by integrating complex causal considerations into model training, thus providing strategies for implementing fair models in real-world applications.