REVIEW 1 cited by
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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.
Discussion (0). Continue with ORCID to comment.