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Causal inference, social networks, and chain graphs
[article]
2020
arXiv
pre-print
Traditionally, statistical and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from
arXiv:1812.04990v2
fatcat:mrovc2h2tjabzpabgd5qsp7l24