Behaviour by individuals or organizations is often interdependent. Social contagion posits that behaviour spreads from unit to unit due to the presence of network or equivalence relations as transmission pathways. Contagion of a single behaviour has been modelled in cross-sectional and temporal data contexts. But existing statistical approaches have not been able to identify multiple contagion pathways in temporal processes where multiple actors can display or adopt multiple behaviours. This data structure and problem setting is common, for example in health behaviours by peers, treaty ratification by states, the spread of wildfire incidents in forests, or the diffusion of policies or political beliefs. We explore the application of bipartite relational event models of actors and behaviours and find that temporally backward-looking specifications confound social contagion with prior similarity, the tendency of similar units to adopt the same behaviour independently. We construct a set of sufficient statistics parsing information bidirectionally along the event sequence to establish an atemporal prior similarity null distribution against which contagion hypotheses for multiple pathways can be tested. Using simulations and four empirical cases, we show the efficacy of this parametric approach for disentangling contagion from prior similarity, contributing to causal inference for temporal networks.
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