Faced with data-driven policies, individuals will manipulate their features to obtain favorable decisions. While earlier works cast these manipulations as undesirable gaming, recent works have adopted a more nuanced causal framing in which manipulations can improve outcomes of interest, and setting coherent mechanisms requires accounting for both predictive accuracy and improvement of the outcome. Typically, these works focus on known causal graphs, consisting only of an outcome and its parents. In this paper, we introduce a general framework in which an outcome and n observed features are related by an arbitrary unknown graph and manipulations are restricted by a fixed budget and cost structure. We develop algorithms that leverage strategic responses to discover the causal graph in a finite number of steps. Given this graph structure, we can then derive mechanisms that trade off between accuracy and improvement. Altogether, our work deepens links between causal discovery and incentive design and provides a more nuanced view of learning under causal strategic prediction.
翻译:面对数据驱动的政策,个人将操纵自己的特征,以获得有利的决定。虽然早期的工程将这些操纵视为不可取的游戏,但最近的工程采用了一个更加细微的因果框架,操纵可以改善感兴趣的结果,建立一致的机制需要既考虑到预测的准确性,又考虑到结果的改进。通常,这些工程侧重于已知的因果图表,只包括结果及其父母。在本文中,我们引入了一个总框架,将结果和观察到的特征与任意的未知图表联系起来,操纵受到固定预算和成本结构的限制。我们开发了一种算法,利用战略对策,在有限的步骤中发现因果图表。鉴于这个图表结构,我们随后可以得出准确性和改进之间的交换机制。总的来说,我们的工作加深了因果发现与激励设计之间的联系,并在因果战略预测下提供更加细微的学习观点。