declare_inquiry: Declare inquiry

In DeclareDesign: Declare and Diagnose Research Designs

Declare inquiry

Description

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

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.

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.

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))

# Some common inquiries
design +
  declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0))

design +
  declare_inquiry(difference_in_var = var(Y_Z_1) - var(Y_Z_0))

design +
  declare_inquiry(mean_Y = mean(Y))

# Inquiries among a subset
design +
  declare_inquiry(ATT = mean(Y_Z_1 - Y_Z_0),
                  subset = (Z == 1))

design +
  declare_inquiry(CATE = mean(Y_Z_1 - Y_Z_0),
                  subset = X == 1)
                  
# equivalently
design +
  declare_inquiry(CATE = mean(Y_Z_1[X == 1] - Y_Z_0[X == 1]))

# 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)

# 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)

# 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)


# 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)

DeclareDesign documentation built on May 29, 2024, 11:10 a.m.