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.

本研究提出了一种简单且通用的非参数估计框架，用于在公平性约束下估计异质性处理效应，该方法在标准正则条件下展现出双重鲁棒性质，通过评估方法，我们确定了公平和最大福利之间的权衡，并用实际案例展示了我们的方法。

公平且健壮的异质性处理效应评估用于政策学习

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy  Learning

We study minimax methods for off-policy evaluation (OPE) using value
functions and marginalized importance weights. Despite that they hold promises
of overcoming the exponential variance in traditional importance sampling,
several key problems remain:
(1) They require function approximation and are generally biased. For the
sake of trustworthy OPE, is there anyway to quantify the biases?
(2) They are split into two styles ("weight-learning" vs "value-learning").
Can we unify them?
In this paper we answer both questions positively. By slightly altering the
derivation of previous methods (one from each style; Uehara et al., 2020), we
unify them into a single value interval that comes with a special type of
double robustness: when either the value-function or the importance-weight
class is well specified, the interval is valid and its length quantifies the
misspecification of the other class. Our interval also provides a unified view
of and new insights to some recent methods, and we further explore the
implications of our results on exploration and exploitation in off-policy
policy optimization with insufficient data coverage.

该研究使用价值函数和边际重要性权重研究了最小极小化方法在离线策略评估中的应用，结合两种不同风格的方法，提出了一个特殊类型的双重稳健方法，解决了偏见问题，同时还探索了其在数据覆盖不足的离线策略优化中对探索和开发的影响。

离线评估和策略优化的极小极大值区间

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization

Rejoinder to ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]

针对一篇关于不完整数据中估计总体均值的研究，提出了对于双重稳健性的回应。

解读反驳：解析双重鲁棒性：比较从不完整数据中估计总体均值的备选策略

Rejoinder: Demystifying Double Robustness: A Comparison of Alternative  Strategies for Estimating a Population Mean from Incomplete Data

Comment on ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]

探讨使用双重稳健法和替代策略估算不完整数据中总体均值的方法。