  Observational studies are regarded as economic alternatives to randomized
trials, often used in their stead to investigate and determine treatment
efficacy. Due to lack of sample size, observational studies commonly combine
data from multiple sources or different sites/centers. Despite the benefits of
an increased sample size, a naive combination of multicenter data may result in
incongruities stemming from center-specific protocols for generating cohorts or
reactions towards treatments distinct to a given center, among other things.
These issues arise in a variety of other contexts, including capturing a
treatment effect related to an individual's unique biological characteristics.
Existing methods for estimating heterogeneous treatment effects have not
adequately addressed the multicenter context, but rather treat it simply as a
means to obtain sufficient sample size. Additionally, previous approaches to
estimating treatment effects do not straightforwardly generalize to the
multicenter design, especially when required to provide treatment insights for
patients from a new, unobserved center. To address these shortcomings, we
propose Multiple Domain Causal Networks (MDCN), an approach that simultaneously
strengthens the information sharing between similar centers while addressing
the selection bias in treatment assignment through learning of a new feature
embedding. In empirical evaluations, MDCN is consistently more accurate when
estimating the heterogeneous treatment effect in new centers compared to
benchmarks that adjust solely based on treatment imbalance or general center
differences. Finally, we justify our approach by providing theoretical analyses
that demonstrate that MDCN improves on the generalization bound of the new,
unobserved target center.
