  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.
