Abstract
Decision-makers are often experts of their domain and take actions based on their domain knowledge. Doctors, for instance, may prescribe treatments by predicting the likely outcome of each available treatment. Actions of an expert thus naturally encode part of their domain knowledge, and can help make inferences within the same domain: Knowing doctors try to prescribe the best treatment for their patients, we can tell treatments prescribed more frequently are likely to be more effective. Yet in machine learning, the fact that most decision-makers are experts is often overlooked, and "expertise" is seldom leveraged as an inductive bias. This is especially true for the literature on treatment effect estimation, where often the only assumption made about actions is that of overlap. In this paper, we argue that expertise - particularly the type of expertise the decision-makers of a domain are likely to have - can be informative in designing and selecting methods for treatment effect estimation. We formally define two types of expertise, predictive and prognostic, and demonstrate empirically that: (i) the prominent type of expertise in a domain significantly influences the performance of different methods in treatment effect estimation, and (ii) it is possible to predict the type of expertise present in a dataset, which can provide a quantitative basis for model selection.
Abstract (translated)
决策者通常是它们领域内的专家，并根据其专业知识采取行动。例如，医生可能会通过预测每个可用的治疗方案的可能结果来开处方。专家的行动因此自然地编码了他们在领域的一部分知识，并有助于在同一领域进行推断。了解医生试图为他们的患者开最好的治疗方案，我们可以告诉我们，开药频率较高的治疗方案可能更有效。然而，在机器学习领域，忽视了大多数决策者都是专家这一事实，并且很少利用“专业知识”作为归纳偏见。这尤其对于治疗效果估计的文献，其中通常只做出一种假设：治疗方案之间存在重叠。在本文中，我们 argue that expertise -特别是决策者领域内可能拥有的专业知识类型 - 在设计和选择治疗效果估计方法时是有信息量的。我们正式定义了两种专业知识类型：预测和预见。并通过经验证明了：（i）领域内专业知识突出地影响了不同方法在治疗效果估计中的表现， （ii）可以预测数据集中的专业知识类型，从而为模型选择提供定量基础。
URL
https://arxiv.org/abs/2403.00694
