Defining Expertise: Applications to Treatment Effect Estimation
专业能力的定义及其处理效应估计应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论专业能力作为归纳偏置对处理效应估计及方法选择的影响。
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专业能力的定义及其处理效应估计应用
- 研究问题
- 决策者掌握的专业知识如何体现在行动中，不同类型的专业能力是否影响处理效应估计方法表现，并能否为模型选择提供依据？
- 方法与思路
- 作者形式化定义 predictive 与 prognostic 两类专业能力，实证比较其对处理效应估计方法表现的影响，并研究从数据预测能力类型。
- 材料陈述的贡献
- 摘要称专业能力的主要类型显著影响不同估计方法的表现，且数据中的能力类型可以预测，从而为方法选择提供量化依据。
- 阅读关注点
- 应核对两类专业能力的正式定义、行动所包含的信息与混杂的关系，以及能力类型预测和估计方法选择是否在独立数据上验证。
- 阅读边界
- 这是 arXiv 预印本，未核实同行评审或期刊接收。摘要未说明因果识别条件和实验细节，不能由处理被选择的频率直接判断其效果。
原文摘要
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.