We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging recent advances in conformal prediction, CARD employs machine learning techniques to accurately identify responders while controlling the false discovery rate in finite sample sizes. Additionally, we incorporate a propensity score adjustment to mitigate bias arising from non-random treatment allocation, enhancing the robustness of our method in observational settings. Simulation studies demonstrate that CARD effectively detects responders with high power in diverse scenarios.

翻译：本文提出因果响应者检测（CARD），一种用于响应者分析的新方法，旨在识别对处理产生显著响应的受试个体。该方法基于近期在保形预测方面的进展，利用机器学习技术精确识别响应者，并在有限样本量下控制错误发现率。此外，我们引入了倾向得分调整以减轻非随机处理分配带来的偏差，从而增强方法在观测性研究中的稳健性。模拟研究表明，CARD在多种场景下均能以高功效有效检测出响应者。