Package: txshift 0.3.9

txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in 'sl3', available for download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Authors:Nima Hejazi [aut, cre, cph], David Benkeser [aut], Iván Díaz [ctb], Jeremy Coyle [ctb], Mark van der Laan [ctb, ths]

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Bug tracker:https://github.com/nhejazi/txshift/issues

On CRAN:

causal-effectscausal-inferencecensored-datamachine-learningrobust-statisticsstatisticsstochastic-interventionsstochastic-treatment-regimestargeted-learningtreatment-effectsvariable-importance

2 exports 13 stars 1.76 score 59 dependencies 19 scripts 229 downloads

Last updated 12 months agofrom:b51b2b42fb. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:msm_vimshifttxshift

Dependencies:abindassertthatclicodetoolscolorspacedata.tabledigestdplyrfansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegtablehal9001haldensifyisobanditeratorslabelinglatex2explatticelifecyclelistenvlsplinemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmeorigamiparallellypillarpkgconfigR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangscalesshapestringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr