Integrating information from multiple data sources can enable more precise, timely, and generalizable decisions. However, it is challenging to make valid causal inferences using observational data from multiple data sources. For example, in healthcare, learning from electronic health records contained in different hospitals is desirable but difficult due to heterogeneity in patient case mix, differences in treatment guidelines, and data privacy regulations that preclude individual patient data from being pooled. Motivated to overcome these issues, we develop a federated causal inference framework. We devise a doubly robust estimator of the mean potential outcome in a target population and show that it is consistent even when some models are misspecified. To enable real-world use, our proposed algorithm is privacy-preserving (requiring only summary statistics to be shared between hospitals) and communication-efficient (requiring only one round of communication between hospitals). We implement our causal estimation and inference procedure to investigate the quality of hospital care provided by a diverse set of 51 candidate Cardiac Centers of Excellence, as measured by 30-day mortality and length of stay for acute myocardial infarction (AMI) patients. We find that our proposed federated global estimator improves the precision of treatment effect estimates by 59% to 91% compared to using data from the target hospital alone. This precision gain results in qualitatively different conclusions about the estimated effect of percutaneous coronary intervention (PCI) compared to medical management (MM) in 63% (32 of 51) of hospitals. We find that hospitals rarely excel in both PCI and MM, which highlights the importance of assessing performance on specific treatment regimens.

翻译：整合多个数据源的信息有助于实现更精准、更及时且更具普适性的决策。然而，利用来自多个数据源的观测数据进行有效的因果推断仍面临挑战。例如，在医疗领域，学习不同医院电子健康记录中的数据虽具价值，但受限于患者病例组合的异质性、治疗指南的差异以及禁止汇集个体患者数据的数据隐私法规。为解决上述问题，我们提出了一种联邦因果推断框架。我们设计了一种对目标人群平均潜在结局的双稳健估计量，并证明即使在部分模型设定错误的情况下，该估计量仍具有一致性。为促进实际应用，所提出的算法具备隐私保护特性（仅需医院间共享汇总统计量）及通信高效性（仅需医院间一轮通信）。我们利用该因果估计与推断流程，以30天死亡率和急性心肌梗死（AMI）患者的住院时长为指标，评估了由51家候选心脏诊疗卓越中心组成的多样化医院群体的服务质量。结果表明，与仅使用目标医院数据相比，我们提出的联邦全局估计量将治疗效果估计的精度提升了59%至91%。这一精度提升导致在63%（51家医院中的32家）的医院中，对经皮冠状动脉介入治疗（PCI）与药物治疗（MM）的估计效果得出了性质不同的结论。此外，我们发现医院在PCI和MM两种治疗手段上极少同时表现优异，这凸显了针对特定治疗方案进行绩效评估的重要性。