plot.dbw: Summarize and plot covariate balance

In dbw: Doubly Robust Distribution Balancing Weighting Estimation

Summarize and plot covariate balance

Description

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

Usage

## S3 method for class 'dbw'
plot(
  x,
  addcov = NULL,
  standardize = TRUE,
  plot = TRUE,
  absolute = TRUE,
  threshold = 0,
  sort = TRUE,
  ...
)

Arguments

x	an object of class “dbw”, usually, a result of a call to dbw.
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 distribution balancing weighting.
...	other graphical parameters (see par).

Details

Estimated weights are normalized to sum to one. Position of the legend is determined internally. Set absolute = TRUE and sort = TRUE for choosing a better location automatically.

Value

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 distribution balancing 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, the control group for the average treatment effects on the controlled estimation and the whole population for the other quantity of interest). The differences in the categorical variables are not standardized.

Author(s)

Hiroto Katsumata

Examples

# Simulation from Kang and Shafer (2007) and Imai and Ratkovic (2014)
# ATE estimation
# True ATE is 10
tau <- 10
set.seed(12345)
n <- 1000
X <- matrix(stats::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, prop)
y <- 210 + 
     27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] + 
     tau * treat + stats::rnorm(n = n, mean = 0, sd = 1)
ybinom <- (y > 210) + 0
df0 <- data.frame(X, treat, y, ybinom)
colnames(df0) <- c("x1", "x2", "x3", "x4", "treat", "y", "ybinom")

# Variables for 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 formulas for propensity score estimation
df <- data.frame(df0, Xmis)
formula_ps_c <- stats::as.formula(treat ~ x1 + x2 + x3 + x4)
formula_ps_m <- stats::as.formula(treat ~ x1mis + x2mis + 
                                          x3mis + x4mis)

# Formula for a misspecified outcome model
formula_y <- stats::as.formula(y ~ x1mis + x2mis + x3mis + x4mis)

# Set plot margins
oldpar <- par(no.readonly = TRUE) # Just for adjusting plot margins
par(mar = c(5.1, 5.1, 4.1, 2.1)) # Just for adjusting plot margins

# Misspecified propensity score model

# Distribution balancing weighting without regularization
fitdbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
               estimand = "ATE", method = "dbw",
               method_y = "wls", data = df, normalize = TRUE,
               vcov = TRUE, lambda = 0, weights = NULL,
               clevel = 0.95)
plot(fitdbwm, addcov = ~ x1 + x2 + x3 + x4, threshold = c(0.1, 0.2))

# Non-absolute values
plot(fitdbwm, addcov = ~ x1 + x2 + x3 + x4, absolute = FALSE, 
     threshold = c(0.1, 0.2))

# No sorting
plot(fitdbwm, addcov = ~ x1 + x2 + x3 + x4, 
     threshold = c(0.1, 0.2), sort = FALSE)


# Covariate balancing weighting function without regularization
fitcbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
               estimand = "ATE", method = "cb",
               method_y = "wls", data = df, normalize = TRUE,
               vcov = TRUE, lambda = 0, weights = NULL,
               clevel = 0.95)
plot(fitcbwm, addcov = ~ x1 + x2 + x3 + x4, threshold = c(0.1, 0.2))

# Entropy balancing weighting function without regularization
fitebwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
               estimand = "ATE", method = "eb",
               method_y = "wls", data = df, normalize = TRUE,
               vcov = TRUE, lambda = 0, weights = NULL,
               clevel = 0.95)
plot(fitebwm, addcov = ~ x1 + x2 + x3 + x4, threshold = c(0.1, 0.2))

# Standard logistic regression
fitmlem <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
               estimand = "ATE", method = "mle",
               method_y = "wls", data = df, normalize = FALSE,
               vcov = TRUE, lambda = 0, weights = NULL,
               clevel = 0.95)
plot(fitmlem, addcov = ~ x1 + x2 + x3 + x4, threshold = c(0.1, 0.2))

par(oldpar)

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

dbw documentation built on Sept. 11, 2024, 6:50 p.m.