论文标题
因果战略线性回归
Causal Strategic Linear Regression
论文作者
论文摘要
在许多预测性决策方案（例如信用评分和学术测试）中，决策者必须建立一个模型，该模型通过更改其功能来说明代理商“游戏”决策规则的倾向，从而获得更好的决策。尽管战略分类文献以前曾假定代理人的结果并不受其特征的因果影响（因此战略代理人的目标是欺骗决策者），但我们将同时的工作加入建模代理的结果，作为其可变可属性的函数。作为我们的主要贡献，我们为学习决策规则提供了有效的算法，该规则在可实现的线性环境中优化了三个不同的决策制造目标：准确预测代理的胶结后结果（预测风险最小化），激励代理人改善这些结果（代理结果最大化），并估计了估计估算的模型（参数估算）。我们的算法避免了Miller等人的硬度结果。 （2020）允许决策者测试一系列决策规则并观察代理人的反应，实际上是通过决策规则执行因果干预的。
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously assumed that agents' outcomes are not causally affected by their features (and thus that strategic agents' goal is deceiving the decision-maker), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents' post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents' responses, in effect performing causal interventions through the decision rules.
