nw_est {causaldrf}  R Documentation 
This is a kernel based regression method that uses a kernel as a weighting function to estimate the ADRF. The normal kernel is weighted by the inverse of the estimated GPS. See Flores et al. (2012) for more details.
nw_est(Y, treat, treat_formula, data, grid_val, bandw, treat_mod, link_function, ...)
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 
data 
is a dataframe containing 
grid_val 
contains the treatment values to be evaluated. 
bandw 
is the bandwidth. Default is 1. 
treat_mod 
a description of the error distribution to be used in the
model for treatment. Options include: 
link_function 
is either "log", "inverse", or "identity" for the
"Gamma" 
... 
additional arguments to be passed to the treatment regression function. 
This method is a version of the NadaryaWatson estimator Nadaraya (1964) which is a local constant regression but weighted by the inverse of the estimated GPS.
nw_est
returns an object of class "causaldrf",
a list that contains the following components:
param 
parameter estimates for a nw fit. 
t_mod 
the result of the treatment model fit. 
call 
the matched call. 
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric doseresponse models. Manuscript in preparation.
Flores, Carlos A., et al. "Estimating the effects of length of exposure to instruction in a training program: the case of job corps." Review of Economics and Statistics 94.1 (2012): 153171.
Nadaraya, Elizbar A. "On estimating regression." Theory of Probability \& Its Applications 9.1 (1964): 141–142.
nw_est
, iw_est
, hi_est
, gam_est
,
add_spl_est
,
bart_est
, etc. for other estimates.
t_mod
, overlap_fun
to prepare the data
for use in the different estimates.
## Example from Schafer (2015). example_data < sim_data nw_list < nw_est(Y = Y, treat = T, treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8, data = example_data, grid_val = seq(8, 16, by = 1), bandw = bw.SJ(example_data$T), treat_mod = "Normal") sample_index < sample(1:1000, 100) plot(example_data$T[sample_index], example_data$Y[sample_index], xlab = "T", ylab = "Y", main = "nw estimate") lines(seq(8, 16, by = 1), nw_list$param, lty = 2, lwd = 2, col = "blue") legend('bottomright', "nw estimate", lty=2, lwd = 2, col = "blue", bty='Y', cex=1) rm(example_data, nw_list, sample_index)