ATE.ERROR (version 1.0.0)

ATE.ERROR.XY: ATE.ERROR.XY Function for Estimating Average Treatment Effect (ATE) with Measurement Error in X and Misclassification in Y

Description

The ATE.ERROR.XY function implements a method for estimating the Average Treatment Effect (ATE) that accounts for both measurement error in covariates and misclassification in the binary outcome variable Y.

Usage

ATE.ERROR.XY(
  Y_star,
  A,
  Z,
  X_star,
  p11,
  p10,
  sigma_epsilon,
  B = 100,
  Lambda = seq(0, 2, by = 0.5),
  extrapolation = "linear",
  bootstrap_number = 250
)

Value

A list containing:

summary

A data frame with the following columns:

• Naive_ATE: Naive estimate of the ATE.

• Sigma_epsilon: The covariance matrix Sigma_epsilon for the measurement error model.

• p10: The probability of misclassified Y given Y = 0.

• p11: The probability of correctly classified Y given Y = 1.

• Extrapolation: A regression model used for extrapolation ("linear", "quadratic", "nonlinear").

• ATE: Mean ATE estimate from the bootstrap samples.

• SE: Standard error of the ATE estimate.

• CI: 95% confidence interval for the ATE estimate.

boxplot

A ggplot object representing the boxplot of the ATE estimates.

Arguments

Y_star

Numeric vector. The observed binary outcome variable, possibly misclassified.

A

Numeric vector. The treatment indicator (1 if treated, 0 if control).

Z

Numeric vector. A precisely measured covariate vector.

X_star

Numeric vector. A covariate vector subject to measurement error.

p11

Numeric. The probability of correctly classified Y given Y = 1.

p10

Numeric. The probability of misclassified Y given Y = 0.

sigma_epsilon

Numeric. The covariance matrix Sigma_epsilon for the measurement error model.

B

Integer. The number of simulated datasets.

Lambda

Numeric vector. A sequence of lambda values for simulated datasets.

extrapolation

Character. A regression model used for extrapolation ("linear", "quadratic", "nonlinear").

bootstrap_number

Numeric. The number of bootstrap samples (default is 250).

Details

The ATE.ERROR.XY function is designed to handle measurement error in covariates and misclassification in outcomes by using the augmented simulation-extrapolation approach.

Examples

# \donttest{
library(ATE.ERROR)
data(Simulated_data)
Y_star <- Simulated_data$Y_star
A <- Simulated_data$T
Z <- Simulated_data$Z
X_star <- Simulated_data$X_star
p11 <- 0.8
p10 <- 0.2
sigma_epsilon <- 0.1
B <- 100
Lambda <- seq(0, 2, by = 0.5)
bootstrap_number <- 10
result <- ATE.ERROR.XY(Y_star, A, Z, X_star, p11, p10, sigma_epsilon, B, Lambda, 
                       "linear", bootstrap_number)
print(result$summary)
print(result$boxplot)
# }