R/medoutcon.R
medoutcon.RdEfficient estimation of stochastic interventional (in)direct effects
medoutcon(
W,
A,
Z,
M,
Y,
R = rep(1, length(Y)),
obs_weights = rep(1, length(Y)),
svy_weights = NULL,
two_phase_weights = rep(1, length(Y)),
effect = c("direct", "indirect"),
contrast = NULL,
g_learners = sl3::Lrnr_glm_fast$new(),
h_learners = sl3::Lrnr_glm_fast$new(),
b_learners = sl3::Lrnr_glm_fast$new(),
q_learners = sl3::Lrnr_glm_fast$new(),
r_learners = sl3::Lrnr_glm_fast$new(),
u_learners = sl3::Lrnr_hal9001$new(),
v_learners = sl3::Lrnr_hal9001$new(),
d_learners = sl3::Lrnr_glm_fast$new(),
estimator = c("tmle", "onestep"),
estimator_args = list(cv_folds = 5L, max_iter = 5L, tiltmod_tol = 5)
)A matrix, data.frame, or similar object corresponding
to a set of baseline covariates.
A numeric vector corresponding to a treatment variable. The
parameter of interest is defined as a location shift of this quantity.
A numeric vector corresponding to an intermediate confounder
affected by treatment (on the causal pathway between the intervention A,
mediators M, and outcome Y, but unaffected itself by the mediators). When
set to NULL, the natural (in)direct effects are estimated.
A numeric vector, matrix, data.frame, or
similar corresponding to a set of mediators (on the causal pathway between
the intervention A and the outcome Y).
A numeric vector corresponding to an outcome variable.
A logical vector indicating whether a sampled observation's
mediator was measured via a two-phase sampling design. Defaults to a
vector of ones, implying that two-phase sampling was not performed.
A numeric vector of observation-level weights. The
default is to give all observations equal weighting.
A numeric vector of observation-level weights that
have been computed externally, such as survey sampling weights. Such
weights are used in the construction of re-weighted efficient estimators.
A numeric vector of known observation-level
weights corresponding to the inverse probability of the mediator being
measured. Defaults to a vector of ones.
A character indicating whether to compute the direct or
the indirect effect as discussed in <https://arxiv.org/abs/1912.09936>.
This is ignored when the argument contrast is provided. By default,
the direct effect is estimated.
A numeric double indicating the two values of the
intervention A to be compared. The default value of NULL has
no effect, as the value of the argument effect is instead used to
define the contrasts. To override effect, provide a numeric
double vector, giving the values of a' and a*, e.g., c(0, 1).
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a model for the propensity score.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a model for a parameterization of the
propensity score that conditions on the mediators.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a model for the outcome regression.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a model for a nuisance regression of
the intermediate confounder, conditioning on the treatment and potential
baseline covariates.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a model for a nuisance regression of
the intermediate confounder, conditioning on the mediators, the treatment,
and potential baseline confounders.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a pseudo-outcome regression required
for in the efficient influence function.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit a pseudo-outcome regression required
for in the efficient influence function.
A Stack object, or other learner class
(inheriting from Lrnr_base), containing instantiated
learners from sl3; used to fit an initial efficient influence
function regression when computing the efficient influence function in a
two-phase sampling design.
The desired estimator of the direct or indirect effect (or contrast-specific parameter) to be computed. Both an efficient one-step estimator using cross-fitting and a cross-validated targeted minimum loss estimator (TMLE) are available. The default is the TML estimator.
A list of extra arguments to be passed (via
...) to the function call for the specified estimator. The default
is chosen so as to allow the number of folds used in computing the one-step
or TML estimators to be easily adjusted. In the case of the TML estimator,
the number of update (fluctuation) iterations is limited, and a tolerance
is included for the updates introduced by the tilting (fluctuation) models.
# here, we show one-step and TML estimates of the interventional direct
# effect; the indirect effect can be evaluated by a straightforward change
# to the penultimate argument. the natural direct and indirect effects can
# be evaluated by omitting the argument Z (inappropriate in this example).
# create data: covariates W, exposure A, post-exposure-confounder Z,
# mediator M, outcome Y
n_obs <- 200
w_1 <- rbinom(n_obs, 1, prob = 0.6)
w_2 <- rbinom(n_obs, 1, prob = 0.3)
w <- as.data.frame(cbind(w_1, w_2))
a <- as.numeric(rbinom(n_obs, 1, plogis(rowSums(w) - 2)))
z <- rbinom(n_obs, 1, plogis(rowMeans(-log(2) + w - a) + 0.2))
m_1 <- rbinom(n_obs, 1, plogis(rowSums(log(3) * w + a - z)))
m_2 <- rbinom(n_obs, 1, plogis(rowSums(w - a - z)))
m <- as.data.frame(cbind(m_1, m_2))
y <- rbinom(n_obs, 1, plogis(1 / (rowSums(w) - z + a + rowSums(m))))
# one-step estimate of the interventional direct effect
os_de <- medoutcon(
W = w, A = a, Z = z, M = m, Y = y,
effect = "direct",
estimator = "onestep"
)
# TML estimate of the interventional direct effect
# NOTE: improved variance estimate and de-biasing from targeting procedure
tmle_de <- medoutcon(
W = w, A = a, Z = z, M = m, Y = y,
effect = "direct",
estimator = "tmle"
)