Installation
About
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")'.
Citation | txshift citation info |
github.com/nhejazi/txshift | |
Bug report | File report |
Key Metrics
Downloads
Yesterday | 16 +100% |
Last 7 days | 51 -4% |
Last 30 days | 210 -2% |
Last 90 days | 621 -28% |
Last 365 days | 2.733 -9% |