Heterogeneous Treatment and Spillover Effects under Clustered Network Interference

Understand

The bulk of causal inference studies rule out the presence of interference between units.

• However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network.
• In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and neighborhood interference within clusters.
• The proposed Network Causal Tree (NCT) algorithm has several advantages.