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The causal inference literature has increasingly recognized that explicitlytargeting treatment effect heterogeneity can lead to improved scientificunderstanding and policy recommendations. Towards the same ends, studying thecausal pathway connecting the treatment to the outcome can be also useful. Thispaper addresses these problems in the context of \emph{causal mediationanalysis}. We introduce a varying coefficient model based on Bayesian additiveregression trees to identify and regularize heterogeneous causal mediationeffects; analogously with linear structural equation models, these effectscorrespond to covariate-dependent products of coefficients. We show that, evenon large datasets with few covariates, LSEMs can produce highly unstableestimates of the conditional average direct and indirect effects, while our\emph{Bayesian causal mediation forests} model produces estimates that arestable. We find that our approach is conservative, with effect estimates``shrunk towards homogeneity.'' We examine the salient properties of our methodusing both data from the Medical Expenditure Panel Survey andempirically-grounded simulated data. Finally, we show how our model can becombined with posterior summarization strategies to identify interestingsubgroups and interpret the model fit.

Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles