Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks

© 2020, © 2020 American Statistical Association. Abstract–Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended—say,...
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