A doubly robust estimator and inference procedure for treatment policies that maximize the average outcome of the worst-off alpha fraction of the population.
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
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abstract
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.
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econ.EM 1years
2025 1verdicts
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Policy Learning with $\alpha$-Expected Welfare
A doubly robust estimator and inference procedure for treatment policies that maximize the average outcome of the worst-off alpha fraction of the population.