Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, and Hoffman (), traditional point treatment, and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Estimation is enhanced using the Super Learner from 'sl3' available for download from GitHub using 'remotes::install_github("tlverse/[email protected]")'.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("lmtp")

0.0.5 by Nicholas Williams, 16 days ago


Browse source code at https://github.com/cran/lmtp


Authors: Nicholas Williams [aut, cre, cph] , Iván Díaz [aut, cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports slider, stats, nnls, cli, utils, R6, generics, origami, future, progressr

Suggests testthat, covr, rmarkdown, knitr, ranger, twang

Enhances sl3


See at CRAN