LF_shift.RdShifts a likelihood factor according to a shift_function.
In effect, get_likelihood(tmle_task) will instead the likelihood from the original_lf,
but for shifted value \(A'=\)shift_function\((A,W)\)
LF_shift
R6Class object.
LF_base object
define_lf(LF_shift, name, type = "density", original_lf, shift_function, ...)
namecharacter, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem
original_lfLF_base object, the likelihood factor to shift
shift_functionfunction, defines the shift
shift_inversefunction, the inverse of shift_function
...Not currently used.
original_lfLF_base object, the likelihood factor to shift
shift_functionfunction, defines the shift
shift_inversefunction, the inverse of shift_function
Díaz, Iván, and Mark J van der Laan. 2017. “Stochastic Treatment Regimes.” In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, 167–80. Springer Science & Business Media. Muñoz, Iván Díaz, and Mark J van der Laan. 2012. “Population Intervention Causal Effects Based on Stochastic Interventions.” Biometrics 68 (2). Wiley Online Library: 541–49.
Other Likelihood objects: CF_Likelihood,
LF_base, LF_emp,
LF_fit, LF_static,
Likelihood,
Targeted_Likelihood,
define_lf