Abstract: For machine-based prediction of visitation, a graph of paired mobile devices is formed where the edges for each pair are based on similarities in visitation and/or metadata for the devices. A machine-learned network embeds the visitation and metadata information, which is used to indicate the similarity. Since the trace data used to show access may be sparse, another machine-learned network completes the route based on routes used by similar devices. Another machine-learned network recommends effectiveness of content based on routes, the graph, metadata, and/or other information. The recommendation is based on training using counterfactual and/or other causal modeling.