Causal inference for community-based multi-layered intervention study

Estimating causal treatment effect for randomized controlled trials under post-treatment confounding, that is, noncompliance and informative dropouts, is becoming an important problem in intervention/prevention studies when the treatment exposures are not completely controlled. When confounding is present in a study, the traditional intention-to-treat approach could underestimate the treatment effect because of insufficient exposure of treatment. In the recent two decades, many papers have been published to address such confounders to investigate the causal relationship between treatment and outcome of interest based on different modeling strategies. Most of the existing approaches, however, are suitable only for standard experiments. In this paper, we propose a new class of structural functional response model to address post-treatment confounding in complex multi-layered intervention studies within a longitudinal data setting. The new approach offers robust inference and is readily implemented. We illustrate and assess the performance of the proposed structural functional response model using both real and simulated data.