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, Hoffman, and Schenck , 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.


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install.packages("lmtp")

1.0.0 by Nicholas Williams, a month ago


https://github.com/nt-williams/lmtp


Report a bug at https://github.com/nt-williams/lmtp/issues


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


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


Documentation:   PDF Manual  


AGPL-3 license


Imports stats, nnls, cli, utils, R6, generics, origami, future, progressr, data.table, SuperLearner

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


See at CRAN