Identifying Causal Effects in Experiments with Social Interactions and Non-compliance
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This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of social interactions–one person's treatment may affect another's outcome–and one-sided non-compliance–subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person's own treatment changes her outcome, while indirect effects quantify how her peers' treatments change her outcome. We consider the case in which social interactions occur only within known groups, and take-up decisions do not depend on peers' offers. In this setting we point identify local average treatment effects, both direct and indirect, in a flexible random coefficients model that allows for both heterogenous treatment effects and endogeneous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases.
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