iptw_est {causaldrf}  R Documentation 
The inverse probability of treatment weighting (iptw) estimator
Description
The iptw method or importance weighting method estimates the ADRF by weighting the data with stabilized or nonstabilized weights.
Usage
iptw_est(Y,
treat,
treat_formula,
numerator_formula,
data,
degree,
treat_mod,
link_function,
...)
Arguments
Y 
is the the name of the outcome variable contained in 
treat 
is the name of the treatment variable contained in

treat_formula 
an object of class "formula" (or one that can be
coerced to that class) that regresses 
numerator_formula 
an object of class "formula" (or one that can be
coerced to that class) that regresses 
data 
is a dataframe containing 
degree 
is 1 for linear and 2 for quadratic outcome model. 
treat_mod 
a description of the error distribution to be used in the
model for treatment. Options include: 
link_function 
specifies the link function between the variables in
numerator or denominator and exposure, respectively.
For 
... 
additional arguments to be passed to the low level treatment regression fitting functions. 
Details
This method uses inverse probability of treatment weighting to adjust for possible biases. For more details see Schafer and Galagate (2015) and Robins, Hernan, and Brumback (2000).
Value
iptw_est
returns an object of class "causaldrf",
a list that contains the following components:
param 
parameter estimates for a iptw fit. 
t_mod 
the result of the treatment model fit. 
num_mod 
the result of the numerator model fit. 
weights 
the estimated weights. 
weight_data 
the weights. 
out_mod 
the outcome model. 
call 
the matched call. 
References
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric doseresponse models. Manuscript in preparation.
van der Wal, Willem M., and Ronald B. Geskus. "IPW: an R package for inverse probability weighting." Journal of Statistical Software 43.13 (2011): 123.
Robins, James M and Hernan, Miguel Angel and Brumback, Babette. Marginal structural models and causal inference in epidemiology. Epidemiology 11.5 (2000): 550–560.
Zhu, Yeying and Coffman, Donna L and Ghosh, Debashis. A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments. Journal of Causal Inference 3.1 (2015): 25–40.
See Also
iptw_est
, ismw_est
,
reg_est
, aipwee_est
, wtrg_est
,
etc. for other estimates.
t_mod
, overlap_fun
to prepare the data
for use in the different estimates.
Examples
## Example from Schafer (2015).
example_data < sim_data
iptw_list < iptw_est(Y = Y,
treat = T,
treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
numerator_formula = T ~ 1,
data = example_data,
degree = 1,
treat_mod = "Normal")
sample_index < sample(1:1000, 100)
plot(example_data$T[sample_index],
example_data$Y[sample_index],
xlab = "T",
ylab = "Y",
main = "iptw estimate")
abline(iptw_list$param[1],
iptw_list$param[2],
lty=2,
lwd = 2,
col = "blue")
legend('bottomright',
"iptw estimate",
lty=2,
lwd = 2,
col = "blue",
bty='Y',
cex=1)
rm(example_data, iptw_list, sample_index)
## Example from van der Wal, Willem M., and Ronald B. Geskus. (2011)
#Simulate data with continuous confounder and outcome, binomial exposure.
#Marginal causal effect of exposure on outcome: 10.
n < 1000
simdat < data.frame(l = rnorm(n, 10, 5))
a.lin < simdat$l  10
pa < exp(a.lin)/(1 + exp(a.lin))
simdat$a < rbinom(n, 1, prob = pa)
simdat$y < 10*simdat$a + 0.5*simdat$l + rnorm(n, 10, 5)
simdat[1:5,]
temp_iptw < iptw_est(Y = y,
treat = a,
treat_formula = a ~ l,
numerator_formula = a ~ 1,
data = simdat,
degree = 1,
treat_mod = "Binomial",
link_function = "logit")
temp_iptw[[1]] # estimated coefficients