R-crumble

Implements a modern, unified estimation strategy for common mediation estimands (natural effects, organic effects, interventional effects, and recanting twins) in combination with modified treatment policies as described in: Liu, Williams, Rudolph, and Díaz (2024) . Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning.

homepage: https://cran.r-project.org/web/packages/crumble/index.html

version	toolchain
0.1.2	foss/2023b

(quick links: (all) - 0 - a - b - c - d - e - f - g - h - i - j - k - l - m - n - o - p - q - r - s - t - u - v - w - x - y - z)