# NOT RUN {
# Set up a design stub for use in examples:
my_population <- declare_population(N = 100, X = rnorm(N))
my_potential_outcomes <- declare_potential_outcomes(
Y ~ (.25 + X) * Z + rnorm(N))
my_assignment <- declare_assignment(m = 50)
design_stub <- my_population + my_potential_outcomes + my_assignment +
declare_reveal()
# Get example data to compute estimands on
dat <- draw_data(design_stub)
# ----------
# 1. Single estimand
# ----------
# Declare an average treatment effect (ATE) estimand
my_estimand_ATE <- declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0))
my_estimand_ATE(dat)
# or a conditional estimand
my_estimand_ATT <- declare_estimand(ATT = mean(Y_Z_1 - Y_Z_0),
subset = (Z == 1))
my_estimand_ATT (dat)
# Add estimands to a design along with estimators that reference them
my_estimator <- declare_estimator(Y ~ Z,
estimand = my_estimand_ATE, label = "estimator")
design_one <- design_stub + my_estimand_ATE + my_estimator
draw_estimands(design_one)
# ----------
# 2. Multiple estimands
# ----------
# You can also specify multiple estimands for a single estimator
# With multiple estimands, you can use one estimator for both...
my_estimator_two <- declare_estimator(Y ~ Z,
estimand = c(my_estimand_ATE, my_estimand_ATT))
design_two <- design_stub + my_estimand_ATE +
my_estimand_ATT + my_estimator_two
draw_estimands(design_two)
# ----------
# 3. Paired estimands / estimators from a single model
# ----------
# For convenience you can also declare multiple estimands
# simultaneously and connect these to the corresponding
# terms for estimates used in the mode.
# Name your estimands the term name they get in your
# estimator, and set `term = TRUE`
estimands_regression <- declare_estimand(
`(Intercept)` = mean(Y_Z_0),
`Z` = mean(Y_Z_1 - Y_Z_0),
term = TRUE,
label="Regression_Estimands"
)
# For the model based estimator, specify the estimand as usual,
# but also set `term = TRUE`
estimators_regression <- declare_estimator(
Y ~ Z,
estimand = estimands_regression,
model = lm,
term = TRUE
)
design_regression <- design_stub + estimands_regression +
estimators_regression
run_design(design_regression)
# ----------
# 4. Custom estimand function
# ----------
# You can declare more complex estimands by defining custom
# estimand functions:
estimand_function <- function(data, label) {
ret <- with(data, median(Y_Z_1 - Y_Z_0))
data.frame(estimand_label = label,
estimand = ret,
stringsAsFactors = FALSE)
}
estimand_custom <- declare_estimand(handler = estimand_function,
label = "medianTE")
estimand_custom(dat)
# Use with custom estimators
estimator_function <- function(data){
data.frame(estimate = with(data, median(Y)))
}
estimator_custom <-
declare_estimator(handler = tidy_estimator(estimator_function),
estimand = estimand_custom)
design_custom <- design_stub + estimand_custom +
estimator_custom
run_design(design_custom)
# ----------
# 5. Batch estimands and estimators
# ----------
# You can declare a group of estimands with distinct labels
# in one go and link them manually to a group of estimators.
# In this case you can add a \code{term} argument to the
# custom estimators to identify them.
f1 <- function(data) {
data.frame(estimand_label = c("control", "ate"),
estimand = with(data, c(mean(Y_Z_0), mean(Y_Z_1 - Y_Z_0))),
stringsAsFactors = FALSE)
}
estimands <- declare_estimand(handler = f1)
f2 <- function(data) data.frame(estimate = with(data,
c(mean(Y[Z == 0]),
mean(Y[Z == 1]) - mean(Y[Z == 0]))),
term = 1:2)
estimators <- declare_estimator(handler = tidy_estimator(f2),
estimand = c("control", "ate"), label = "custom")
design <- design_stub + estimands + estimators
# }
# NOT RUN {
diagnose_design(design, sims = 20, bootstrap_sims = FALSE,
diagnosands = declare_diagnosands(
select = c(mean_estimate, mean_estimand)))
# }
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