Abstract
Much of the existing causal inference literature focuses on the effect of a single or multiple time-point intervention on an outcome based on observing longitudinal data on n independent units that are not causally connected. The causal effect is then defined as an expectation of the effect of the intervention assigned to the unit on their outcome, and causal effects of the intervention on other units on the unit’s outcome are assumed nonexistent. As a consequence, causal models only have to be concerned with causal relations between the components of the unit-specific data structure. Statistical inference is based on the assumption that the n data structures are n independent realizations of a random variable. However, in many CRTs or observational studies of few communities, the number of independent units is not large enough to allow statistical inference based on limit distributions.
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Notes
- 1.
See references in Sect. 20.9 for implications of incorrectly ignoring interference.
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Sofrygin, O., van der Laan, M.J. (2018). Causal Inference in Longitudinal Network-Dependent Data. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_20
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