ismw_est | R Documentation |
This method estimates the ADRF by using weighting matrices instead of
scalars. The weight matrices require conditional expectations of the
treatment and higher order conditional expectations. It uses outputs from
the t_mod
function.
ismw_est(Y, treat, data, e_treat_1, e_treat_2, e_treat_3, e_treat_4, degree )
Y |
is the the name of the outcome variable contained in |
treat |
is the name of the treatment variable contained in
|
data |
is a dataframe containing |
e_treat_1 |
a vector, representing the conditional expectation of
|
e_treat_2 |
a vector, representing the conditional expectation of
|
e_treat_3 |
a vector, representing the conditional expectation of
|
e_treat_4 |
a vector, representing the conditional expectation of
|
degree |
is 1 for linear and 2 for quadratic outcome model. |
This function estimates the ADRF requires estimated moments and uses the outputs of the t_mod function as inputs. For more details, see Schafer and Galagate (2015).
ismw_est
returns an object of class "causaldrf_simple",
a list that contains the following components:
param |
the estimated parameters. |
call |
the matched call. |
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.
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.
## Example from Schafer (2015). example_data <- sim_data t_mod_list <- t_mod(treat = T, treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8, data = example_data, treat_mod = "Normal") cond_exp_data <- t_mod_list$T_data full_data <- cbind(example_data, cond_exp_data) ismw_list <- ismw_est(Y = Y, treat = T, data = full_data, e_treat_1 = full_data$est_treat, e_treat_2 = full_data$est_treat_sq, e_treat_3 = full_data$est_treat_cube, e_treat_4 = full_data$est_treat_quartic, degree = 1) sample_index <- sample(1:1000, 100) plot(example_data$T[sample_index], example_data$Y[sample_index], xlab = "T", ylab = "Y", main = "ismw estimate") abline(ismw_list$param[1], ismw_list$param[2], lty=2, lwd = 2, col = "blue") legend('bottomright', "ismw estimate", lty=2, lwd = 2, col = "blue", bty='Y', cex=1) rm(example_data, t_mod_list, cond_exp_data, full_data, ismw_list, sample_index)
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