  This paper investigates the case of interference, when a unit's treatment
also affects other units' outcome. When interference is at work, policy
evaluation mostly relies on the use of randomized experiments under cluster
interference and binary treatment. Instead, we consider a non-experimental
setting under continuous treatment and network interference. In particular, we
define spillover effects by specifying the exposure to network treatment as a
weighted average of the treatment received by units connected through physical,
social or economic interactions. We provide a generalized propensity
score-based estimator to estimate both direct and spillover effects of a
continuous treatment. Our estimator also allows to consider asymmetric network
connections characterized by heterogeneous intensities. To showcase this
methodology, we investigate whether and how spillover effects shape the optimal
level of policy interventions in agricultural markets. Our results show that,
in this context, neglecting interference may underestimate the degree of policy
effectiveness.
