  When estimating a Global Average Treatment Effect (GATE) under network
interference, units can have widely different relationships to the treatment
depending on a combination of the structure of their network neighborhood, the
structure of the interference mechanism, and how the treatment was distributed
in their neighborhood. In this work, we introduce a sequential procedure to
generate and select graph- and treatment-based covariates for GATE estimation
under regression adjustment. We show that it is possible to simultaneously
achieve low bias and considerably reduce variance with such a procedure. To
tackle inferential complications caused by our feature generation and selection
process, we introduce a way to construct confidence intervals based on a block
bootstrap. We illustrate that our selection procedure and subsequent estimator
can achieve good performance in terms of root mean squared error in several
semi-synthetic experiments with Bernoulli designs, comparing favorably to an
oracle estimator that takes advantage of regression adjustments for the known
underlying interference structure. We apply our method to a real world
experimental dataset with strong evidence of interference and demonstrate that
it can estimate the GATE reasonably well without knowing the interference
process a priori.
