  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. First, it allows the investigation of the treatment
effect heterogeneity, avoiding potential bias due to the presence of
interference. Second, understanding the heterogeneity of both treatment and
spillover effects can guide policy-makers in scaling up interventions,
designing targeting strategies, and increasing cost-effectiveness. We
investigate the performance of our NCT method using a Monte Carlo simulation
study, and we illustrate its application to assess the heterogeneous effects of
information sessions on the uptake of a new weather insurance policy in rural
China.
