44 citations · 125 across the 18 of their papers we have counts for
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stat.ME2017
Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed
Caleb H. Miles, Maya Petersen, Mark J. van der Laan
The assumption that no subject's exposure affects another subject's outcome, known as the no-interference assumption, has long held a foundational position in the study of causal i…
stat.ME2017★ 43 cited
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Laura B. Balzer, Wenjing Zheng, Mark J. van der Laan +1
We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of healt…