Ratio of Mediator Probability Weighting for Estimating Natural Direct and Indirect Effects

By Guanglei Hong. 2025

70 citations recorded by the source corpus.

Decomposing a total causal effect into natural direct and indirect effects is central to revealing causal mechanisms. Conventional methods achieve the decomposition by specifying an outcome model as a linear function of the treatment, the mediator, and the observed covariates under identification assumptions including the assumption of no interaction between treatment and mediator. Recent statistical advances relax this assumption typically within the linear or nonlinear regression framework. I propose a non-parametric approach that also relaxes the assumption of no treatment-mediator interaction while avoiding the problems of outcome model specification that become particularly acute in the presence of a large number of covariates. The key idea is to estimate the marginal mean of each counterfactual outcome by assigning a weight to every experimental unit such that the weighted distribution of the mediator under the experimental condition approximates the counterfactual mediator distribution under the control condition. The weight is a ratio of the conditional probability of a mediator value under the control condition to that of the same mediator value under the experimental condition. A non-parametric approach to estimating the weight on the basis of propensity score stratification promises to increase the robustness of the direct and indirect effect estimates. The outcome is modeled as a function of the direct and indirect effects with minimal model-based assumptions. This method applies regardless of the distribution of the outcome or the functional relationship between the outcome and the mediator, and is suitable for handling a large number of pretreatment covariates. RMPW software packages are available in Stata (https://ideas.repec.org/c/boc/bocode/s458301.html) and R (https://cran.r-project.org/web/packages/rmpw/index.html).

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a76c0465a53cfbc04f49e6e79c29b03bea5e029a

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Citations in the corpus

Imputation Strategies for the Estimation of Natural Direct and Indirect Effects (2012, methodology): “The doubly robust estimator is closely related to a doubly robust estimator in Tchetgen Tchetgen and Shpitser (2011) and can be viewed as a compromise between the inverse probability weighted estimator of Hong (2010) and Lange et al.”
Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables (2014, methodology): “More recent research has focused on non-parametric and semiparametric identification, e.g. Pearl (2001), Robins (2003), Hong (2010), Imai et al. (2010), Tchetgen Tchetgen and Shpitser (2012) and Huber (2014).”
A simple unified approach for estimating natural direct and indirect effects. (2012, methodology): “The estimation procedure is related to work by Hong (16) but generalized to a broad class of outcomes.”
Causal mediation analysis in economics: Objectives, assumptions, models (2021, background): “Some examples are Robins and Greenland (1992); Pearl (2001); Petersen, Sinisi and van der Laan (2006); VanderWeele (2009); Imai, Keele and Yamamoto (2010); Hong (2010); Albert and Nelson (2011); Tchetgen Tchetgen and Shpister (2012); Vansteelandt, Bekaert and Lange (2012) from many others.”