Shifts 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

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_shift, name, type = "density", original_lf, shift_function, ...)

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

original_lf

LF_base object, the likelihood factor to shift

shift_function

function, defines the shift

shift_inverse

function, the inverse of shift_function

...

Not currently used.

Fields

original_lf

LF_base object, the likelihood factor to shift

shift_function

function, defines the shift

shift_inverse

function, the inverse of shift_function

References

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.

See also