We consider the problem of learning fair policies for multi-stage selection
problems from observational data. This problem arises in several high-stakes
domains such as company hiring, loan approval, or bail decisions where outcomes
(e.g., career success, loan repayment, recidivism) are only observed for those
selected. We propose a multi-stage framework that can be augmented with various
fairness constraints, such as demographic parity or equal opportunity. This
problem is a highly intractable infinite chance-constrained program involving
the unknown joint distribution of covariates and outcomes. Motivated by the
potential impact of selection decisions on people's lives and livelihoods, we
propose to focus on interpretable linear selection rules. Leveraging tools from
causal inference and sample average approximation, we obtain an asymptotically
consistent solution to this selection problem by solving a mixed binary conic
optimization problem, which can be solved using standard off-the-shelf solvers.
We conduct extensive computational experiments on a variety of datasets adapted
from the UCI repository on which we show that our proposed approaches can
achieve an 11.6% improvement in precision and a 38% reduction in the measure of
unfairness compared to the existing selection policy.

通过解决难以计算的无限机会约束程序，提出了一种多阶段框架，用于从观测数据中学习公平策略，该问题与公司招聘、贷款批准或保释决策等高风险领域中的选择相关，我们专注于可解释的线性选择规则，并通过解决混合二次锥优化问题获得了组合评价的解决方案，与现有的选择策略相比，在精确度方面提高了 11.6%，在不公平度方面减少了 38%。