Package 'tmle3mediate'

Likelihood Factor for Incremental Propensity Score Interventions

Description

Likelihood Factor for Incremental Propensity Score Interventions

Format

R6Class object.

Value

LF_base object.

Constructor

define_lf(LF_ipsi, name, type = "density", likelihood_base, shift_param, treatment_task, control_task, ...)

name

A character, giving the name of the likelihood factor. Should match a node name in the nodes specified by the npsem slot of tmle3_Task.

likelihood_base

A trained Likelihood object, for use in generating a re-scaled likelihood factor.

shift_param

A numeric, specifying the magnitude of the desired incremental propensity score shift (a multiplier of the odds of receiving treatment).

treatment_task

A tmle3_Task object created by setting the intervention to the treatment condition: do(A = 1).

control_task

A tmle3_Task object created by setting the intervention to the control condition: do(A = 0).

...

Not currently used.

Fields

likelihood_base

A trained Likelihood object, for use in generating a re-scaled likelihood factor.

shift_param

A numeric, specifying the magnitude of the desired incremental propensity score shift (a multiplier of the odds of receiving treatment).

treatment_task

A tmle3_Task object created by setting the intervention to the treatment condition: do(A = 1).

control_task

A tmle3_Task object created by setting the intervention to the control condition: do(A = 0).

...

Additional arguments passed to the base class.

References

"Nonparametric Causal Effects Based on Incremental Propensity Score Interventions."

Kennedy, Edward H (2019). Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2017.1422737

"Causal Mediation Analysis for Stochastic Interventions"

Díaz, Iván and Hejazi, Nima S (2020). Journal of the Royal Statistical Society, Series B. https://doi.org/10.1111/rssb.12362

---

Parameter for the Population Intervention (In)direct Effects

Description

Parameter definition class. See https://doi.org/10.1111/rssb.12362.

Format

R6Class object.

Value

Param_base object.

Constructor

define_param(Param_medshift, shift_param, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood.

shift_param

A numeric, specifying the magnitude of the desired incremental propensity score shift (a multiplier of the odds of receiving treatment).

...

Not currently used.

outcome_node

A character, giving the name of the node that should be treated as the outcome.

Fields

cf_likelihood

The counterfactual likelihood under the joint stochastic intervention on exposure and mediators.

lf_ipsi

Object derived from LF_base for assessing the joint intervention on exposure and mediators.

treatment_task

A tmle3_Task created by setting the intervention to the treatment condition: do(A = 1).

control_task

A tmle3_Task object created by setting the intervention to the control condition: do(A = 0).

shift_param

A numeric, specifying the magnitude of the desired incremental propensity score shift (a multiplier of the odds of receiving treatment).

See Also

Other Parameters: Param_NDE, Param_NIE

---

Parameter for the natural direct effect

Description

Parameter definition class. See https://www.ncbi.nlm.nih.gov/pubmed/22499725

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_NDE, observed_likelihood, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood.

...

Not currently used.

outcome_node

A character, giving the name of the node that should be treated as the outcome.

Fields

cf_likelihood_treatment

The counterfactual likelihood for the treatment.

cf_likelihood_control

The counterfactual likelihood for the control.

treatment_task

tmle3_Task created by setting the intervention to the treatment condition: do(A = 1).

control_task

tmle3_Task created by setting the intervention to the control condition: do(A = 0).

See Also

Other Parameters: Param_NIE, Param_medshift

---

Parameter for the natural indirect effect

Description

Parameter definition class. See https://www.ncbi.nlm.nih.gov/pubmed/22499725

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_NIE, observed_likelihood, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood.

...

Not currently used.

outcome_node

A character, giving the name of the node that should be treated as the outcome.

Fields

cf_likelihood_treatment

The counterfactual likelihood for the treatment.

cf_likelihood_control

The counterfactual likelihood for the control.

treatment_task

tmle3_Task created by setting the intervention to the treatment condition: do(A = 1).

control_task

tmle3_Task created by setting the intervention to the control condition: do(A = 0).

See Also

Other Parameters: Param_NDE, Param_medshift

---

TML Estimator for the Population Intervention (In)direct Effects

Description

O = (W, A, Z, Y) W = Covariates (possibly multivariate) A = Treatment (binary or categorical) Z = Mediators (binary or categorical; possibly multivariate) Y = Outcome (binary or bounded continuous)

Usage

tmle_medshift(
  shift_type = "ipsi",
  delta,
  e_learners,
  phi_learners,
  max_iter = 10000,
  step_size = 1e-06,
  ...
)

Arguments

shift_type	A character defining the type of shift to be applied to the exposure – an incremental propensity score intervention.
delta	A numeric, specifying the magnitude of the shift.
e_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in fitting a cleverly parameterized propensity score that conditions on the mediators, i.e., $e = P(A \mid Z, W)$.
phi_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in a regression of a pseudo-outcome on the baseline covariates, i.e., $phi(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) | W)]$.
max_iter	A numeric setting the maximum iterations allowed in the targeting step based on universal least favorable submodels.
step_size	A numeric giving the step size (delta_epsilon in tmle3) to be used in the targeting step based on universal least favorable submodels.
...	Additional arguments (currently unused).

---

TML Estimator for the Natural Direct Effect

Description

O = (W, A, Z, Y) W = Covariates (possibly multivariate) A = Treatment (binary or categorical) Z = Mediators (binary or categorical; possibly multivariate) Y = Outcome (binary or bounded continuous)

Usage

tmle_NDE(e_learners, psi_Z_learners, max_iter = 10000, step_size = 1e-06, ...)

Arguments

e_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in fitting a cleverly parameterized propensity score that conditions on the mediators, i.e., $e = P(A \mid Z, W)$.
psi_Z_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in a regression of a pseudo-outcome on the baseline covariates, i.e., $psi_Z(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) \mid A = 0, W]$.
max_iter	A numeric setting the maximum iterations allowed in the targeting step based on universal least favorable submodels.
step_size	A numeric giving the step size (delta_epsilon in tmle3) to be used in the targeting step based on universal least favorable submodels.
...	Additional arguments (currently unused).

---

TML Estimator for the Natural Indirect Effect

Description

O = (W, A, Z, Y) W = Covariates (possibly multivariate) A = Treatment (binary or categorical) Z = Mediators (binary or categorical; possibly multivariate) Y = Outcome (binary or bounded continuous)

Usage

tmle_NIE(e_learners, psi_Z_learners, max_iter = 10000, step_size = 1e-06, ...)

Arguments

e_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in fitting a cleverly parameterized propensity score that conditions on the mediators, i.e., $e = P(A \mid Z, W)$.
psi_Z_learners	A Stack (or other learner class that inherits from Lrnr_base), containing a single or set of instantiated learners from sl3, to be used in a regression of a pseudo-outcome on the baseline covariates, i.e., $psi_Z(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) \mid A = 0, W]$.
max_iter	A numeric setting the maximum iterations allowed in the targeting step based on universal least favorable submodels.
step_size	A numeric giving the step size (delta_epsilon in tmle3) to be used in the targeting step based on universal least favorable submodels.
...	Additional arguments (currently unused).

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TML Estimator for the Population Intervention (In)direct Effects

Description

TML Estimator for the Population Intervention (In)direct Effects

---

TML Estimator for the Natural Direct Effect

Description

TML Estimator for the Natural Direct Effect

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TML Estimator for the Natural Indirect Effect

Description

TML Estimator for the Natural Indirect Effect

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