| Name : R-txshift
| |
| Version : 0.3.8
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.9.2
| Date : 2024-06-14 12:09:34
|
| Group : Development/Libraries/Other
| Source RPM : R-txshift-0.3.8-lp153.9.2.src.rpm
|
| Size : 0.21 MB
| |
| Packager : (none)
| |
| Summary : Efficient Estimation of the Causal Effects of Stochastic Interventions
|
Description :
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\")\'.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
