Randomized experiments, or “A/B” tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference needed for credible causal inference. Existing solutions include accounting for the fraction or count of treated neighbors in a user’s network, among other strategies. Yet, there are often a high number of researcher degrees of freedom in specifying network interference conditions and most current methods do not account for the local network structure beyond simply counting the number of neighbors. Capturing local network structures is important because it can account for theories, such as structural diversity and echo chambers. Our study provides an approach that accounts for both the local structure in a user’s social network via motifs as well as the assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ “causal network motifs”, i.e. network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. We test our method on a real-world experiment on a large-scale network and a synthetic network setting, which highlight how accounting for local structures can better account for different interference patterns in networks.