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nhejazi

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")'.

Maintained by Nima Hejazi. Last updated 6 months ago.

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

9.0 match 14 stars 5.12 score 19 scripts

pik-piam

mredgebuildings:Prepare data to be used by the EDGE-Buildings model

Prepare data to be used by the EDGE-Buildings model.

Maintained by Robin Hasse. Last updated 3 days ago.

1.6 match 3.72 score