Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, policies, or interventions), referred to as \textit{composite treatments}, on a set of outcome variables of interest, referred to as \textit{composite outcomes}, for a unit from observational data -- remains a fundamental problem in causal inference with applications across disciplines, such as healthcare, economics, education, social science, marketing, and computer science. Previous work in causal machine learning for ITE estimation is limited to simple settings, like single treatments and single outcomes. This hinders their use in complex real-world scenarios; for example, consider studying the effect of different ICU interventions, such as beta-blockers and statins for a patient admitted for heart surgery, on different outcomes of interest such as atrial fibrillation and in-hospital mortality. The limited research into composite treatments and outcomes is primarily due to data scarcity for all treatments and outcomes. To address the above challenges, we propose a novel and innovative hypernetwork-based approach, called \emph{H-Learner}, to solve ITE estimation under composite treatments and composite outcomes, which tackles the data scarcity issue by dynamically sharing information across treatments and outcomes. Our empirical analysis with binary and arbitrary composite treatments and outcomes demonstrates the effectiveness of the proposed approach compared to existing methods.

翻译：个体化处理效应（ITE）的估计——即从观测数据中推断一组变量（亦称为暴露、处理、行动、政策或干预，统称为\\textit{复合处理}）对一组目标结果变量（称为\\textit{复合结果}）在个体单元上的因果效应——仍是因果推断领域的核心问题，其应用遍及医疗保健、经济学、教育学、社会科学、市场营销及计算机科学等多个学科。先前针对ITE估计的因果机器学习研究多局限于简单场景，如单一处理与单一结果，这限制了其在复杂现实情境中的应用；例如，研究不同重症监护室干预措施（如为接受心脏手术的患者使用β受体阻滞剂和他汀类药物）对不同关注结果（如心房颤动和院内死亡率）的影响。针对复合处理与结果的研究匮乏，主要源于所有处理与结果的数据稀缺性。为应对上述挑战，我们提出一种新颖且创新的基于超网络的方法，称为\\emph{H-Learner}，以解决复合处理与复合结果下的ITE估计问题，该方法通过动态共享跨处理与结果的信息来应对数据稀缺问题。我们在二元及任意复合处理与结果下的实证分析表明，相较于现有方法，所提方法具有显著有效性。