Description Usage Arguments Details Value
aiptw
Estimates the parameter of interest (E[Y_d]) by directly solving the efficient influence function as an estimating equation.
1 2 |
data |
data frame following the time-ordering of the nodes. |
cum.g |
a matrix of the cumulative probabilities of treatment (and being uncensored) given the parents. |
Ynodes |
column names or indicies in |
Anodes |
column names or indicies in |
Cnodes |
column names or indicies in |
abar |
binary vector (numAnodes x 1) of counterfactual treatment |
Qform |
character vector of regression formulas for Q. |
SL.library |
optional character vector of libraries to pass to SuperLearner. NULL indicates glm should be called instead of SuperLearner. |
stratify |
if |
Please refer to van der Laan and Gruben (2007) for details regarding derivation of the efficient influence function, which this estimator solves.
BangRobinsDR
returns a list of items as an object of class aiptw
, which include
The estimate of the parameter value under the intervention abar
The empirical influence function for the point estimate
The time-specific empirical influence functions
The conditional expectation fits for Qbar
The call to the function
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