Compositional Models for Estimating Causal Effects

Many real-world systems can be usefully represented as sets of interacting components. Examples include computational systems, such as query processors and compilers; natural systems, such as cells and ecosystems; and social systems, such as families and organizations. However, current approaches to estimating *potential outcomes* and *causal effects* typically treat such systems as single units, represent them with a fixed set of variables, and assume a homogeneous data-generating process. In this work, we study a *compositional* approach for estimating individual-level potential outcomes and causal effects in structured systems, where each unit is represented by an *instance-specific* composition of multiple heterogeneous components. The compositional approach decomposes unit-level causal queries into more fine-grained queries, explicitly modeling how unit-level interventions affect component-level outcomes to generate a unit’s outcome. We demonstrate this approach using modular neural network architectures and show that it provides benefits for causal effect estimation from observational data, such as accurate causal effect estimation for structured units, increased sample efficiency, improved overlap between treatment and control groups, and compositional generalization to units with unseen combinations of components. Remarkably, our results show that compositional modeling can improve the accuracy of causal estimation even when component-level outcomes are unobserved. We also create and use a set of real-world evaluation environments for the empirical evaluation of compositional approaches for causal effect estimation and demonstrate the role of composition structure, varying amounts of component-level data access, and component heterogeneity in the performance of compositional models as compared to the non-compositional approaches.