medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R

Science is most often concerned with questions of mechanism. In myriad applications, only the portion of the causal effect of an exposure on an outcome through a particular pathway under study is of interest. The study of such path-specific, or mediation, effects has a rich history, first undertaken scientifically by Today, the study of such effects has attracted a great deal of attention in statistics and causal inference, inspired by applications in disciplines ranging from epidemiology and vaccinology to psychology and economics. Examples include understanding the biological mechanisms by which vaccines causally alter infection risk (Benkeser et al., 2021; The medoutcon R package provides researchers in each of these disciplines, and in others, with the tools necessary to implement statistically efficient estimators of the interventional direct and indirect effects (Dz et al., 2020) (for brevity, henceforth, (in)direct effects), a recently formulated set of causal effects robust to the presence of confounding of the mediator-outcome relationship by the exposure. In cases where such confounding is a nonissue, the interventional (in)direct effects By readily incorporating the use of machine learning in the estimation of nuisance parameters (through integration with the sl3 R package (Coyle, Hejazi, Malenica, Phillips, & Sofrygin, 2021) of the tlverse ecosystem (van der Laan et al., 2022)), medoutcon incorporates state-of-the-art non/semi-parametric estimation techniques, facilitating their adoption in a vast array of settings.