Before revision:  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.
After revision:  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 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 , and illustrate my results using data from an experiment that analyzes the effect of social interactions in the household on voting behavior.