tstmle3 | R Documentation |
This function estimates the causal effect of a single time-point intervention on the outcome within the same time point. Here, time is defined as observing a single intervention and outcome, and possibly multiple covariates. Intervention is imposed within each unit of time.
tstmle3( data, node_list, parameter = "ATE", learner_list, Co = 5, cvtmle = FALSE, fold_fn = "folds_rolling_origin", first_window = 100, validation_size = 50, gap = 0, batch = 50, window_size = 100 )
data |
data.frame object containing the data. |
node_list |
node list reflecting the relationships between variables. |
parameter |
target parameter for targeting. Default is the average over time Context-Specific Average Treatment Effect. |
learner_list |
learner list containing the learners used for the conditional expectation of outcome and propensity score. |
Co |
user-specified Markov order for the fixed dimensional summary measure. |
cvtmle |
default is |
fold_fn |
cross-validation scheme, as defined by |
first_window |
first window size used for training. Only relevant if the set cross-validation is
|
validation_size |
number of time points used for validation. |
gap |
number of time points between training and validation set. |
batch |
number of time points added in the next next cross-validation fold. |
window_size |
window size used for training. Only relevant if the set cross-validation is
|
An object of class tstmle
.
Average treatment effect estimated using TMLE.
Average treatment effect estimated using IPTW.
Standard error for the TMLE estimated parameter.
Standard deviation for the TMLE estimated parameter.
Confidence Interval for the TMLE estimated parameter.
Influence function.
Number of steps until convergence of the iterative TMLE.
Initial estimates of g, Q.
Final updates estimates of g, Q and clever covariates.
The full tmle3 fit object.
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