iw_est {causaldrf} | R Documentation |
The inverse weighting estimator (nonparametric method)
Description
This is a nonparametric method that estimates the ADRF by using a local linear
regression of Y
on treat
with weighted kernel function. For
details, see Flores et. al. (2012).
Usage
iw_est(Y,
treat,
treat_formula,
data,
grid_val,
bandw,
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 |
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. |
Details
The ADRF is estimated by
(D_{0}(t) S_{2}(t) - D_{1}(t) S_{1}(t)) / (S_{0}(t) S_{2}(t) - S_{1}^{2}(t))
where
D_{j}(t) = \sum_{i = 1}^{N} \tilde{K}_{h, X} (T_i - t) (T_i - t)^j Y_i
and
S_{j}(t) = \sum_{i = 1}^{N} \tilde{K}_{h, X} (T_i - t) (T_i - t)^j
\tilde{K}_{h, X}(t) = K_{h}(t) / \hat{R}_i(t)
which is a local linear regression.
More details are given in Flores (2012).
Value
iw_est
returns an object of class "causaldrf",
a list that contains the following components:
param |
parameter estimates for a iw fit. |
t_mod |
the result of the treatment model fit. |
call |
the matched call. |
References
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response 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): 153-171.
See Also
nw_est
, iw_est
, hi_est
, gam_est
,
add_spl_est
,
bart_est
, etc. for other estimates.
Examples
## Example from Schafer (2015).
example_data <- sim_data
iw_list <- iw_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$Y[sample_index],
xlab = "T",
ylab = "Y",
main = "iw estimate")
lines(seq(8, 16, by = 1),
iw_list$param,
lty = 2,
lwd = 2,
col = "blue")
legend('bottomright',
"iw estimate",
lty=2,
lwd = 2,
col = "blue",
bty='Y',
cex=1)
rm(example_data, iw_list, sample_index)
## Example from Imai & van Dyk (2004).
data("nmes_data")
head(nmes_data)
# look at only people with medical expenditures greater than 0
nmes_nonzero <- nmes_data[which(nmes_data$TOTALEXP > 0), ]
iw_list <- iw_est(Y = TOTALEXP,
treat = packyears,
treat_formula = packyears ~ LASTAGE + I(LASTAGE^2) +
AGESMOKE + I(AGESMOKE^2) + MALE + RACE3 + beltuse +
educate + marital + SREGION + POVSTALB,
data = nmes_nonzero,
grid_val = seq(5, 100, by = 5),
bandw = bw.SJ(nmes_nonzero$packyears),
treat_mod = "LogNormal")
set.seed(307)
sample_index <- sample(1:nrow(nmes_nonzero), 1000)
plot(nmes_nonzero$packyears[sample_index],
nmes_nonzero$TOTALEXP[sample_index],
xlab = "packyears",
ylab = "TOTALEXP",
main = "iw estimate",
ylim = c(0, 10000),
xlim = c(0, 100))
lines(seq(5, 100, by = 5),
iw_list$param,
lty = 2,
lwd = 2,
col = "blue")
legend('topright',
"iw estimate",
lty=2,
lwd = 2,
col = "blue",
bty='Y',
cex = 1)
abline(0, 0)