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]")'.


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0.0.5 by Nicholas Williams, 6 months ago

Browse source code at

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