Motivated by equilibrium models of labor markets, we develop a formulation of
causal strategic classification in which strategic agents can directly
manipulate their outcomes. As an application, we compare employers that
anticipate the strategic response of a labor force with employers that do not.
We show through a combination of theory and experiment that employers with
performatively optimal hiring policies improve employer reward, labor force
skill level, and in some cases labor force equity. On the other hand, we
demonstrate that performative employers harm labor force utility and fail to
prevent discrimination in other cases.

通过对劳动市场的平衡模型进行研究，我们发展了一种因果战略分类的表述，在这种模型中，战略代理人可以直接操纵他们的结果。作为应用，我们比较了预计劳动力战略反应和不预计劳动力战略反应的雇主。我们通过理论和实验的结合表明，具有表现最优的招聘政策的雇主可以提高雇主回报率、劳动力技能水平，并在某些情况下实现劳动力公平。另一方面，我们证明具有行动表现的雇主会损害劳动力效用，并且在其他情况下无法防止歧视。

反向学习在具有影响力的双边市场中的应用

Learning In Reverse Causal Strategic Environments With Ramifications on  Two Sided Markets

Despite the considerable success enjoyed by machine learning techniques in
practice, numerous studies demonstrated that many approaches are vulnerable to
attacks. An important class of such attacks involves adversaries changing
features at test time to cause incorrect predictions. Previous investigations
of this problem pit a single learner against an adversary. However, in many
situations an adversary's decision is aimed at a collection of learners, rather
than specifically targeted at each independently. We study the problem of
adversarial linear regression with multiple learners. We approximate the
resulting game by exhibiting an upper bound on learner loss functions, and show
that the resulting game has a unique symmetric equilibrium. We present an
algorithm for computing this equilibrium, and show through extensive
experiments that equilibrium models are significantly more robust than
conventional regularized linear regression.

该论文研究了带有多个学习者的对抗性线性回归问题，并通过实验表明，均衡模型相较于传统正则化线性回归，具有更高的鲁棒性。