Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
This work addresses fairness in policy learning for decision-makers, but it appears incremental as it builds on existing nonparametric estimation methods.
The authors tackled the problem of estimating heterogeneous treatment effects under fairness constraints, showing that their estimators achieve double robustness and characterizing the fairness-welfare trade-off for optimal policies.
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum welfare achievable by the optimal policy. We evaluate the methods in a simulation study and illustrate them in a real-world case study.