Many real-world systems can be represented as sets of interacting components.
Examples of such systems include computational systems such as query
processors, natural systems such as cells, and social systems such as families.
Many approaches have been proposed in traditional (associational) machine
learning to model such structured systems, including statistical relational
models and graph neural networks. Despite this prior work, existing approaches
to estimating causal effects typically treat such systems as single units,
represent them with a fixed set of variables and assume a homogeneous
data-generating process. We study a compositional approach for estimating
individual treatment effects (ITE) in structured systems, where each unit is
represented by the composition of multiple heterogeneous components. This
approach uses a modular architecture to model potential outcomes at each
component and aggregates component-level potential outcomes to obtain the
unit-level potential outcomes. We discover novel benefits of the compositional
approach in causal inference - systematic generalization to estimate
counterfactual outcomes of unseen combinations of components and improved
overlap guarantees between treatment and control groups compared to the
classical methods for causal effect estimation. We also introduce a set of
novel environments for empirically evaluating the compositional approach and
demonstrate the effectiveness of our approach using both simulated and
real-world data.

通过分解结构化系统，以及模型设计和计算单元级潜在结果以及整合成单位级潜在结果，实现了对因果效应的估计，并在因果推断中发现了分解方法的新优势：对未知组件组合的反事实结果进行系统泛化，以及与传统方法相比，在治疗和对照组之间提供了更好的重叠保证。