mwe_noncompliance_design.R

## Example of simulating data from a design with one-sided non-compliance

types <- c("Always-Taker", "Never-Taker", "Complier", "Defier")
direct_effect_of_encouragement <- 0.0
proportion_defiers <- 0.0
design <- declare_population(
    N = 500,
    type = sample( types, N, replace = TRUE,
      prob = c(0.1, 0.1, 0.8 - proportion_defiers, proportion_defiers)
    ),
    noise = rnorm(N)
  ) +
  declare_potential_outcomes(
    D ~ case_when(
      Z == 0 & type %in% c("Never-Taker", "Complier") ~ 0,
      Z == 1 & type %in% c("Never-Taker", "Defier") ~ 0,
      Z == 0 & type %in% c("Always-Taker", "Defier") ~ 1,
      Z == 1 & type %in% c("Always-Taker", "Complier") ~ 1
    )
  ) +
  declare_potential_outcomes(
    Y ~ 0.5 * (type == "Complier") * D +
      0.25 * (type == "Always-Taker") * D +
      0.75 * (type == "Defier") * D +
      direct_effect_of_encouragement * Z + noise,
    assignment_variables = c("D", "Z")
  ) +
  declare_estimand(CACE = mean((Y_D_1_Z_1 + Y_D_1_Z_0) / 2 -
                                 (Y_D_0_Z_1 + Y_D_0_Z_0) / 2),
                   subset = type == "Complier") +
  declare_estimand(ITT = mean((Y_D_1_Z_1 + Y_D_1_Z_0) / 2 -
                                 (Y_D_0_Z_1 + Y_D_0_Z_0) / 2)) +
  ##declare_estimand(ITT2 = mean((Y_D_1_Z_1 + Y_D_0_Z_1) / 2 -
  ##                               (Y_D_1_Z_0 + Y_D_1_Z_0) / 2)) +
  declare_assignment(prob = 0.5) +
  declare_reveal(D, assignment_variable = "Z") +
  declare_reveal(Y, assignment_variables = c("D", "Z")) +
  declare_estimator(Y ~ D | Z, model = iv_robust, estimand = "CACE",label='2SLS') +
  declare_estimator(Y ~ Z, model=lm_robust, estimand = "ITT",label='OLS')

designs <- redesign(
  design,
  proportion_defiers = 0, # seq(0, 0.3, length.out = 5),
  direct_effect_of_encouragement = seq(0, 0.3, length.out = 5)
)
plan(multiprocess,workers=8)
bookdesigns_diag <- diagnose_design(designs, bootstrap_sims=0,sims = 1000)
bookdesigns_diag
res <- reshape_diagnosis(bookdesigns_diag)
res %>% select(direct_effect_of_encouragement,'Estimand Label','Estimator Label','Term','Bias','SD Estimate','Mean Estimand')
plan(sequential)