Ratio-of-mediator-probability weighting (RMPW) estimates natural direct and indirect effects by reweighting mediator distributions across treatment conditions rather than modeling the outcome, thereby accommodating treatment-by-mediator interaction. This article brings existing RMPW methods together within a common estimation framework, incorporates a heteroskedastic normal model for continuous mediators, and extends two-step variance estimation to multiple parametric weight components. The framework is illustrated using a national health survey to decompose disparities in cigarette smoking by sexual-minority status and a randomized welfare-to-work experiment to examine pathways from program assignment through employment to maternal depressive symptoms. Targeted simulations evaluate model specification, alternative weighting strategies, treatment-selection adjustment, weight stability, variance estimation, and continuous treatments. Parametric RMPW performed well when models were adequately specified and weights were stable, but correct specification alone did not prevent unstable estimation from concentrated weights. Stratification-based weighting provided some robustness to model misspecification. Standard errors that treated estimated weights as fixed were unreliable, whereas two-step and bootstrap inference performed well under appropriate conditions. The results provide practical guidance for selecting weighting strategies, diagnosing instability, and conducting inference. The methods are implemented in the community-contributed Stata command mmwsmed.

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