  We consider the problem of downlink power control in wireless networks,
consisting of multiple transmitter-receiver pairs communicating with each other
over a single shared wireless medium. To mitigate the interference among
concurrent transmissions, we leverage the network topology to create a graph
neural network architecture, and we then use an unsupervised primal-dual
counterfactual optimization approach to learn optimal power allocation
decisions. We show how the counterfactual optimization technique allows us to
guarantee a minimum rate constraint, which adapts to the network size, hence
achieving the right balance between average and $5^{th}$ percentile user rates
throughout a range of network configurations.
