The causal identification of network effects is fraught with theoretical and statistical difficulties. To guide social network researchers in choosing the right empirical strategy, this survey of network effect models (i) maps out the different types of network effects and levels of analysis, (ii) discusses data and measurement issues related to network effect identification, and (iii) reviews the key challenges and solutions to causally identifying network effects. The survey covers both modeling solutions based on random assignment and network models for observational data.