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Abstract:
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A fundamental assumption usually made in causal inference is that of no interference between individuals (i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals). However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. We consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimates for direct, indirect, total and overall causal effects of treatment strategies in this setting. Using a two-stage randomization procedure, unbiased estimators of the estimates are presented. Variances of the estimators are also developed. The methodology is illustrated in two settings where interference is likely.
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