Laparoscopic surgery has been shown through a number of randomized trials to be an effective form of treatment for cholecystitis. Given this evidence, one natural question for clinical practice is: does the effectiveness of laparoscopic surgery vary among patients? It might be the case that, while the overall effect is positive, some patients treated with laparoscopic surgery may respond positively to the intervention while others do not or may be harmed. In our study, we focus on conditional average treatment effects to understand whether treatment effects vary systematically with patient characteristics. Recent methodological work has developed a meta-learner framework for flexible estimation of conditional causal effects. In this framework, nonparametric estimation methods can be used to avoid bias from model misspecification while preserving statistical efficiency. In addition, researchers can flexibly and effectively explore whether treatment effects vary with a large number of possible effect modifiers. However, these methods have certain limitations. For example, conducting inference can be challenging if black-box models are used. Further, interpreting and visualizing the effect estimates can be difficult when there are multi-valued effect modifiers. In this paper, we develop new methods that allow for interpretable results and inference from the meta-learner framework for heterogeneous treatment effects estimation. We also demonstrate methods that allow for an exploratory analysis to identify possible effect modifiers. We apply our methods to a large database for the use of laparoscopic surgery in treating cholecystitis. We also conduct a series of simulation studies to understand the relative performance of the methods we develop. Our study provides key guidelines for the interpretation of conditional causal effects from the meta-learner framework.

翻译：多项随机试验已证实腹腔镜手术是胆囊炎的有效治疗方式。基于此证据，临床实践面临一个自然问题：腹腔镜手术的疗效是否因患者个体差异而不同？可能存在这样的情况：虽然总体效果为阳性，但接受腹腔镜手术的部分患者可能对干预产生积极反应，而其他患者则无反应甚至受到伤害。在本研究中，我们聚焦条件平均治疗效果，以探究治疗效果是否随患者特征呈现系统性变化。最新方法论研究开发了用于灵活估计条件因果效应的元学习框架。该框架可通过非参数估计方法避免模型误设带来的偏差，同时保持统计效率。此外，研究者能灵活有效地探索治疗效果是否随大量可能的效应修饰因子而变化。然而，这些方法存在某些局限性：例如，若使用黑箱模型进行推断将面临挑战；当存在多值效应修饰因子时，效应估计结果的解读与可视化亦存在困难。本文开发了新型方法，可在异质性治疗效果估计的元学习框架中实现可解释性结果与推断。同时，我们展示了允许进行探索性分析以识别潜在效应修饰因子的方法。将所提方法应用于腹腔镜手术治疗胆囊炎的大型数据库，并通过系列模拟研究评估新方法的相对性能。本研究为元学习框架下条件因果效应的解读提供了关键指南。