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Treatment-induced confounders complicate analyses of time-varying treatmenteffects and causal mediation. Conditioning on these variables naively toestimate marginal effects may inappropriately block causal pathways and mayinduce spurious associations between treatment and the outcome, leading tobias. Although several methods for estimating marginal effects avoid thesecomplications, including inverse-probability-of-treatment-weighted (IPTW)estimation of marginal structural models (MSMs) as well as g- andregression-with-residuals (RWR) estimation of highly constrained structuralnested mean models (SNMMs), each suffers from a set of nontrivial limitations.Specifically, IPTW estimation is inefficient, is difficult to use withcontinuous treatments or mediators, and may suffer from finite-sample bias,while g- and RWR estimation of highly constrained SNMMs for marginal effectsare premised on the unrealistic assumption that there is no effect moderation.In this study, we adapt the method of RWR to estimate marginal effects with aset of moderately constrained SNMMs that accommodate several types oftreatment-by-confounder and/or mediator-by-confounder interaction, therebyrelaxing the assumption of no effect moderation. Through a series of simulationexperiments and empirical examples, we show that this approach outperforms IPTWestimation of MSMs as well as both g- and RWR estimation of highly constrainedSNMMs in which effect moderation is assumed away.

Regression-with-residuals Estimation of Marginal Effects: A Method of Adjusting for Treatment-induced Confounders that may also be Moderators