I discuss how we can make causal claims in social network research. We need some understanding of causal effects for successful network intervention. The issue has come to a head through methodological debate around the controversial results of Christakis and Fowler (2007), who argued that obesity is diffused across friendship networks. Their methods have met much criticism, with arguments that homophily and influence are inevitably confounded in network studies, particularly with latent homophily effects. In studies of real network-based social systems, where it is impossible to measure everything, how can we rule out latent effects which by definition are unobserved? Yet, rather than give up on isolating network influence effects, we can be guided by early thoughtful work on causal inference, including by Sewell Wright and Ronald Fisher. The resolution of this conundrum for networks may not lie in seeking the One True Study that will provide the final conclusion, but a research program that combines Big Data with small (or Thick) data. Across various research literatures on obesity, such a research program is effectively coming into place, with implications for how we understand mechanisms for obesity diffusion. For successful causal inference in social networks, we may need research that crosses scale, with both a microscope on the small and a macroscope on the large.