Spillover Effects in Cluster Randomized Trials with Noncompliance
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Clustered randomized trials (CRTs) are popular in the social sciences to evaluate the efficacy of a new policy or program by randomly assigning one set of clusters to the new policy and the other set to the usual policy. Often, many individuals within a cluster fail to take advantage of the new policy, resulting in noncompliance behaviors. Also, individuals within a cluster may influence each other through treatment spillovers where those who comply with the new policy may affect the outcomes of those who do not. Here, we study the identification of causal effects in CRTs when both noncompliance and treatment spillovers are present. We first show that the standard analysis of CRT data with noncompliance using instrumental variables does not identify the usual complier average causal effect under treatment spillovers. We extend this result and show that no analysis of CRT data can unbiasedly estimate local network causal effects. Finally, we develop bounds for these network causal effects that only require standard instrumental variables analysis. We demonstrate these results with an empirical study of a new deworming intervention in Kenya. We find that given high levels of compliance, we can place informative bounds on the total effect among compliers and that the deworming intervention reduced infections among those complied with their treatment assignment.
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