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)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 0.45042799 0.45042799 0.6504280 0 0.45042799
#> 2 002 0 -0.05733307 -0.05733307 0.1426669 0 -0.05733307
#> 3 003 0 0.22940944 0.22940944 0.4294094 0 0.22940944
#> 4 004 0 -0.01188688 -0.01188688 0.1881131 0 -0.01188688
#> 5 005 0 -0.54085735 -0.54085735 -0.3408573 0 -0.54085735
#> 6 006 0 0.51263288 0.51263288 0.7126329 1 0.71263288
# 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 -5.55e-17
#>
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 0.575
#>
# 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 0.13674750 0.04807476 2.844476
#> 2 difference_in_var 0.0 DIV <NA> 0.01854473 NA NA
#> p.value conf.low conf.high df outcome
#> 1 0.004631529 0.04229315 0.2312019 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 0.2897546 0.05087063 5.695911 2.100901e-08
#> 2 ATT 0.2 estimator Z 0.2897546 0.05087063 5.695911 2.100901e-08
#> conf.low conf.high df outcome
#> 1 0.1898071 0.3897021 498 Y
#> 2 0.1898071 0.3897021 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 0.5098828 estimator (Intercept) 0.5187536 0.03584575 14.471830
#> 2 slope 0.2000000 estimator Z 0.1822586 0.05012282 3.636239
#> p.value conf.low conf.high df outcome
#> 1 7.140318e-40 0.44832602 0.5891811 498 Y
#> 2 3.054480e-04 0.08378031 0.2807368 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 6.085101e-01