A Weighting Method for Assessing Between-Site Heterogeneity in Causal Mediation Mechanism

Abstract

When a multisite randomized trial reveals between-site variation in program impact, methods are needed for further investigating heterogeneous mediation mechanisms across the sites. We conceptualize and identify a joint distribution of site-specific direct and indirect effects under the potential outcomes framework. A method-of-moments procedure incorporating ratio-of-mediator-probability weighting (RMPW) consistently estimates the causal parameters. This strategy conveniently relaxes the assumption of no Treatment × Mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We derive asymptotic standard errors that reflect the sampling variability of the estimated weight. We also offer an easy-to-use R package, , that implements the proposed method. It is freely available at the Comprehensive R Archive Network (http://cran.r-project.org/web/packages/MultisiteMediation).

Two‐step estimation in ratio‐of‐mediator‐probability weighted causal mediation analysis

“…Of interest are not only the population average causal effects but also the between‐site variances of the site‐specific indirect and direct effects. Qin and Hong offer 1 translation, and we are currently evaluating this approach as well as exploring others …”

Multisite Causal Mediation Analysis in the Presence of Complex Sample and Survey Designs and Non-Random Non-Response

“…For individuals who share the same pretreatment characteristics, the weight would transform the mediator distribution in treatment group t to resemble that in treatment group t ′ . Hong () and others proved the identification result for causal mediation analysis in a single site; Qin and Hong () extended this result to multisite causal mediation analysis. Here we extend the result to multisite studies involving complex sample or survey designs and non‐random non‐response by combining the assumptions and the weighting strategies that are associated with sampling selection, treatment selection, non‐response selection and mediator value selection.…”

“…To produce valid statistical inferences that incorporate the sampling uncertainty of the weights in the estimation of the causal parameters, we adopt a solution that extends an M-estimation procedure for single-site and multisite RMPW analysis (Bein et al, 2018;Qin and Hong, 2017). This approach estimates the weights and the site-specific causal effects jointly under a generalized method-of-moments (MOM) framework.…”

“…Earlier research has extended a two-step estimation procedure (Newey, 1984) to single-site (Bein et al, 2018) and multisite (Qin and Hong, 2017) RMPW analysis in which the RMPW weights are estimated. The rationale is to stack the estimating equations from both steps and to solve them simultaneously in the spirit of one-step generalized MOM estimation (Hansen, 1982).…”

“…We innovatively adapt these weighting adjustments to the context of mediation analysis by combining them with the ratio of mediator probability weighting (RMPW) strategy, which is for unpacking the causal mechanism and reducing mediator selection bias. RMPW was initially proposed by Hong (2010Hong ( , 2015 and others (Bein et al, 2018;Hong et al, 2011Hong et al, , 2015Hong and Nomi, 2012;Huber, 2014;Lange et al, 2012;Tchetgen Tchetgen and Shpitser, 2012) and was recently extended to multisite studies by Qin and Hong (2017). This strategy, without invoking functional form assumptions for the outcome model, is particularly flexible for accommodating treatment-by-mediator interactions and is suitable for discrete and continuous mediators and outcomes.…”