Declares inquiries, or the inferential target of interest. Conceptually very close to "estimand" or "quantity of interest".
Usage
declare_inquiry(..., handler = inquiry_handler, label = "inquiry")
declare_inquiries(..., handler = inquiry_handler, label = "inquiry")
declare_estimand(...)
declare_estimands(...)
inquiry_handler(data, ..., subset = NULL, term = FALSE, label)Arguments
- ...
arguments to be captured, and later passed to the handler
- handler
a tidy-in, tidy-out function
- label
a string describing the step
- data
a data.frame
- subset
a subset expression
- term
TRUE/FALSE
Value
a function, I(), that accepts a data.frame as an argument and returns a data.frame containing the value of the inquiry, a^m.
Details
For the default diagnosands, the return value of the handler should have inquiry and estimand columns.
If term is TRUE, the names of ... will be returned in a term column,
and inquiry will contain the step label. This can be used as
an additional dimension for use in diagnosis.
Examples
# Set up a design for use in examples:
## Two-arm randomized experiment
design <-
declare_model(
N = 500,
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_assignment(Z = complete_ra(N = N, m = 250)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z))
head(draw_data(design))
#> ID X U Y_Z_0 Y_Z_1 Z Y
#> 1 001 0 -0.208519648 -0.208519648 -0.008519648 1 -0.008519648
#> 2 002 0 -0.054410133 -0.054410133 0.145589867 0 -0.054410133
#> 3 003 0 -0.270755117 -0.270755117 -0.070755117 1 -0.070755117
#> 4 004 0 0.528851641 0.528851641 0.728851641 1 0.728851641
#> 5 005 0 -0.146049241 -0.146049241 0.053950759 1 0.053950759
#> 6 006 0 -0.007633415 -0.007633415 0.192366585 0 -0.007633415
# Some common inquiries
design +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) -------------------
#>
#> Run of the design:
#>
#> inquiry estimand
#> ATE 0.2
#>
design +
declare_inquiry(difference_in_var = var(Y_Z_1) - var(Y_Z_0))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(difference_in_var = var(Y_Z_1) - var(Y_Z_0)) -
#>
#> Run of the design:
#>
#> inquiry estimand
#> difference_in_var 0
#>
design +
declare_inquiry(mean_Y = mean(Y))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(mean_Y = mean(Y)) ----------------------------
#>
#> Run of the design:
#>
#> inquiry estimand
#> mean_Y 0.597
#>
# Inquiries among a subset
design +
declare_inquiry(ATT = mean(Y_Z_1 - Y_Z_0),
subset = (Z == 1))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(ATT = mean(Y_Z_1 - Y_Z_0), subset = (Z == 1))
#>
#> Run of the design:
#>
#> inquiry estimand
#> ATT 0.2
#>
design +
declare_inquiry(CATE = mean(Y_Z_1 - Y_Z_0),
subset = X == 1)
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(CATE = mean(Y_Z_1 - Y_Z_0), subset = X == 1) -
#>
#> Run of the design:
#>
#> inquiry estimand
#> CATE 0.2
#>
# equivalently
design +
declare_inquiry(CATE = mean(Y_Z_1[X == 1] - Y_Z_0[X == 1]))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> Step 2 (assignment): declare_assignment(Z = complete_ra(N = N, m = 250)) -------
#>
#> Step 3 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 4 (inquiry): declare_inquiry(CATE = mean(Y_Z_1[X == 1] - Y_Z_0[X == 1])) --
#>
#> Run of the design:
#>
#> inquiry estimand
#> CATE 0.2
#>
# Add inquiries to a design along with estimators that
# reference them
diff_in_variances <-
function(data) {
data.frame(estimate = with(data, var(Y[Z == 1]) - var(Y[Z == 0])))
}
design_1 <-
design +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0),
difference_in_var = var(Y_Z_1) - var(Y_Z_0)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z,
inquiry = "ATE",
label = "DIM") +
declare_estimator(handler =
label_estimator(diff_in_variances),
inquiry = "difference_in_var",
label = "DIV")
run_design(design_1)
#> inquiry estimand estimator term estimate std.error statistic
#> 1 ATE 0.2 DIM Z 0.290972045 0.05027035 5.788145
#> 2 difference_in_var 0.0 DIV <NA> 0.002696259 NA NA
#> p.value conf.low conf.high df outcome
#> 1 1.260398e-08 0.1922039 0.3897402 498 Y
#> 2 NA NA NA NA <NA>
# Two inquiries using one estimator
design_2 <-
design +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_inquiry(ATT = mean(Y_Z_1 - Y_Z_0), subset = (Z == 1)) +
declare_estimator(Y ~ Z, inquiry = c("ATE", "ATT"))
run_design(design_2)
#> inquiry estimand estimator term estimate std.error statistic p.value
#> 1 ATE 0.2 estimator Z 0.3239665 0.05010567 6.465666 2.409724e-10
#> 2 ATT 0.2 estimator Z 0.3239665 0.05010567 6.465666 2.409724e-10
#> conf.low conf.high df outcome
#> 1 0.225522 0.4224111 498 Y
#> 2 0.225522 0.4224111 498 Y
# Two inquiries using different coefficients from one estimator
design_3 <-
design +
declare_inquiry(intercept = mean(Y_Z_0),
slope = mean(Y_Z_1 - Y_Z_0)) +
declare_estimator(
Y ~ Z,
.method = lm_robust,
term = TRUE,
inquiry = c("intercept", "slope")
)
run_design(design_3)
#> inquiry estimand estimator term estimate std.error statistic
#> 1 intercept 0.5142614 estimator (Intercept) 0.4682314 0.03617378 12.943945
#> 2 slope 0.2000000 estimator Z 0.2920601 0.05022807 5.814679
#> p.value conf.low conf.high df outcome
#> 1 3.082450e-33 0.3971594 0.5393034 498 Y
#> 2 1.086749e-08 0.1933751 0.3907452 498 Y
# declare_inquiries usage
design_4 <- design +
declare_inquiries(
ATE = mean(Y_Z_1[X == 1] - Y_Z_0[X == 1]),
CATE_X0 = mean(Y_Z_1[X == 0] - Y_Z_0[X == 0]),
CATE_X1 = mean(Y_Z_1[X == 1] - Y_Z_0[X == 1]),
Difference_in_CATEs = CATE_X1 - CATE_X0,
mean_Y = mean(Y))
run_design(design_4)
#> inquiry estimand
#> 1 ATE 2.000000e-01
#> 2 CATE_X0 2.000000e-01
#> 3 CATE_X1 2.000000e-01
#> 4 Difference_in_CATEs -5.551115e-17
#> 5 mean_Y 6.146443e-01