I set up a potential-outcomes framework to analyze spillover effects using instrumental variables. I  show that intention-to-treat parameters aggregate a large number of direct and spillover effects for different compliance types, and hence they may not have a clear causal interpretation. I provide three alternative conditions to point identify average direct and spillover effects on specific subpopulations, by restricting either (i) the number of spillover effects , (ii) the degree of noncompliance, or (iii) the degree of heterogeneity in average causal parameters. I propose simple estimators that are consistent and asymptotically normal under mild conditions , and illustrate my results using data from an experiment that analyzes the effect of social interactions in the household on voting behavior.
I set up a potential-outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of these compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and propose simple estimators that are consistent and asymptotically normal under mild conditions . I use the proposed methods to analyze an experiment on social interactions and voting behavior.
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characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of these compliance types. I
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parameters aggregate a large number of
(ITT) parameters aggregate multiple
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they may
do
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causal interpretation. I provide three alternative conditions to point identify average direct and spillover effects on specific subpopulations, by restricting either (i) the number of spillover effects , (ii) the degree of noncompliance, or (iii) the degree of heterogeneity in average causal parameters. I
link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and
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, and illustrate my results using data from an experiment that analyzes the effect of social interactions in the household on
. I use the proposed methods to analyze an experiment on social interactions and