cbcheck: Summarize and plot covariate balance
In nawtilus: Navigated Weighting for the Inverse Probability Weighting

Description Usage Arguments Details Value Author(s) Examples

Summarizes and plots covariate balance between treatment and control groups before and after the navigated weighting.

cbcheck(
  object,
  addcov = NULL,
  standardize = TRUE,
  plot = TRUE,
  absolute = TRUE,
  threshold = 0,
  sort = TRUE
)

`object`	an object of class “nawt”, usually, a result of a call to `nawt`.
`addcov`	a one-sided formula specifying additional covariates whose balance is checked. Covariates containing NAs are automatically dropped.
`standardize`	a logical value indicating whether weighted mean differences are standardized or not.
`plot`	a logical value indicating whether a covariate balance plot is displayed.
`absolute`	a logical value indicating whether the absolute values of differences in weighted means are used in the covariate balance plot.
`threshold`	an optional numeric vector used as threshold markers in the covariate balance plot.
`sort`	a logical value indicating whether covariates in the covariate balance plot are sorted by the values of differences in the weighted means before the navigated weighting.

Position of the legend is determined internally.

A matrix whose rows are the covariates and columns are the differences in the (un)standardized weighted mean between the treatment and control groups before (diff.un) and after (diff.adj) the navigated weighting. The standardized weighted mean is the weighted mean divided by the standard deviation of the covariate for the target population (the treatment group for the average treatment effects on the treated estimation and the whole population for the other quantity of interest). The differences in the categorical variables are not standardized.

Hiroto Katsumata

# Simulation from Kang and Shafer (2007) and Imai and Ratkovic (2014)
# ATT estimation
# True ATT is 10
tau <- 10
set.seed(12345)
n <- 1000
X <- matrix(rnorm(n * 4, mean = 0, sd = 1), nrow = n, ncol = 4)
prop <- 1 / (1 + exp(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + 0.1 * X[, 4]))
treat <- rbinom(n, 1, 1 - prop)
y <- 210 + 27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] + 
     tau * treat + rnorm(n)
df <- data.frame(X, treat, y)
colnames(df) <- c("x1", "x2", "x3", "x4", "treat", "y")

# A misspecified model
Xmis <- data.frame(x1mis = exp(X[, 1] / 2), 
                   x2mis = X[, 2] * (1 + exp(X[, 1]))^(-1) + 10,
                   x3mis = (X[, 1] * X[, 3] / 25 + 0.6)^3, 
                   x4mis = (X[, 2] + X[, 4] + 20)^2)

# Data frame and a misspecified formula for propensity score estimation
df <- data.frame(df, Xmis)
formula_m <- as.formula(treat ~ x1mis + x2mis + x3mis + x4mis)

# Misspecified propensity score model
# Power weighting function with alpha = 2
fits2m <- nawt(formula = formula_m, outcome = "y", estimand = "ATT", 
               method = "score", data = df, alpha = 2)
cbcheck(fits2m, addcov = ~ x1 + x2 + x3 + x4)

# Covariate balancing weighting function
fitcbm <- nawt(formula = formula_m, outcome = "y", estimand = "ATT", 
               method = "cb", data = df)
cbcheck(fitcbm, addcov = ~ x1 + x2 + x3 + x4)

# Standard logistic regression
fits0m <- nawt(formula = formula_m, outcome = "y", estimand = "ATT", 
               method = "score", data = df, alpha = 0)
cbcheck(fits0m, addcov = ~ x1 + x2 + x3 + x4)

# Display the covariate balance matrix
cb <- cbcheck(fits2m, addcov = ~ x1 + x2 + x3 + x4, plot = FALSE)
cb