Description Usage Arguments Value Details References Examples
ipsi
is used to estimate effects of incremental
propensity score interventions, i.e., estimates of mean outcomes if the odds
of receiving treatment were multiplied by a factor delta.
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y 
Outcome of interest measured at end of study. 
a 
Binary treatment. 
x.trt 
Covariate matrix for treatment regression. 
x.out 
Covariate matrix for outcome regression. 
time 
Measurement time. 
id 
Subject identifier. 
delta.seq 
Sequence of delta increment values for incremental propensity score intervention. 
nsplits 
Integer number of sample splits for nuisance estimation. If

ci_level 
A 
progress_bar 
A 
return_ifvals 
A 
fit 
How nuisance functions should be estimated. Options are "rf" for
random forests via the 
sl.lib 
sl.lib algorithm library for SuperLearner. Default library includes "earth", "gam", "glm", "glmnet", "glm.interaction", "mean", "ranger", "rpart. 
A list containing the following components:
res 
estimates/SEs and uniform CIs for population means. 
res.ptwise 
estimates/SEs and pointwise CIs for population means. 
calpha 
multiplier bootstrap critical value. 
Treatment and covariates are expected to be timevarying and measured
throughout the course of the study. Therefore if n
is the number of
subjects and T
the number of timepoints, then a
, time
,
and id
should all be vectors of length n
xT
, and
x.trt
and x.out
should be matrices with n
xT
rows.
However y
should be a vector of length n
since it is only
measured at the end of the study. The subject ordering should be consistent
across function inputs, based on the ordering specified by id
. See
example below for an illustration.
Kennedy EH. Nonparametric causal effects based on incremental propensity score interventions. arxiv:1704.00211
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