Causal inference in randomized experiments with mediational processes.
This article links the structural equation modeling (SEM) approach with the principal stratification (PS) approach, both of which have been widely used to study the role of intermediate posttreatment outcomes in randomized experiments. Despite the potential benefit of such integration, the 2 approaches have been developed in parallel with little interaction. This article proposes the cross-model translation (CMT) approach, in which parameter estimates are translated back and forth between the PS and SEM models. First, without involving any particular identifying assumptions, translation between PS and SEM parameters is carried out on the basis of their close conceptual connection. Monte Carlo simulations are used to further clarify the relation between the 2 approaches under particular identifying assumptions. The study concludes that, under the common goal of causal inference, what makes a practical difference is the choice of identifying assumptions, not the modeling framework itself. The CMT approach provides a common ground in which the PS and SEM approaches can be jointly considered, focusing on their common inferential problems.
Jo
Booil
B
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA. booil@stanford.edu
D015982:Bias; D015984:Causality; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*