 Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.Causal models are "falsifiable", in that if they do not match data, they must be rejected as invalid.They must also be credible to those close to the phenomena the model intends to explain.