The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
nawtilus provides a procedure for the navigated weighting (NAWT) proposed by Katsumata (2020), which estimates a pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) with the inverse probability weighting where propensity scores are estimated using estimating equations suitable for the parameter of interest. It also provides several tools for summarizing and checking the estimation results, including a covariate balance check and an inverse probability weights plot.
Katsumata, Hiroto. 2020. “Navigated Weighting to Improve Inverse Probability Weighting for Missing Data Problems and Causal Inference.” Working Paper, arxiv:2005.10998.
Katsumata, Hiroto. 2020. nawtilus: Navigated Weighting for the Inverse Probability Weighting. R package version 0.1.4. https://CRAN.R-project.org/package=nawtilus.
You can install the released version of nawtilus from CRAN with:
install.packages("nawtilus")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("hirotokatsumata/nawtilus")This example shows how to use nawtilus for estimation of parameter of interest.
First, load the package and make toy data.
# Load the package
library(nawtilus)
# Make toy data
# 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, prop)
y <- 210 + 27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] +
tau * treat + rnorm(n)
# Data frame
df <- data.frame(X, treat, y)
colnames(df) <- c("x1", "x2", "x3", "x4", "treat", "y")Then, specify a model for propensity score estimation.
formula_c <- as.formula(treat ~ x1 + x2 + x3 + x4)Fit the model for the average treatment effects on the treated (ATT)
estimation using nawt() with the default setting (a power
weighting function with α = 2).
# Power weighting function with alpha = 2
fit <- nawt(formula = formula_c, outcome = "y", estimand = "ATT",
method = "score", data = df, alpha = 2)
#> [1] "Estimate weights for the ATT estimation by (weighted) score conditions"You can summarize the results easily with summary().
summary(fit)
#>
#> Call:
#> nawt(formula = formula_c, outcome = "y", estimand = "ATT", method = "score",
#> data = df, alpha = 2)
#>
#> ATT estimates: 10.54
#>
#> Coefficients:
#> Estimate Std..Error z.value Pr...z..
#> est 10.54356 1.19535 8.8205 0.000e+00 ***
#> (Intercept) 0.01142 0.07642 0.1494 8.812e-01
#> x1 -1.05398 0.12718 -8.2871 2.220e-16 ***
#> x2 0.43165 0.09211 4.6860 2.786e-06 ***
#> x3 -0.21745 0.08829 -2.4629 1.378e-02 *
#> x4 0.12933 0.09039 1.4308 1.525e-01
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Effective N for the propensity score estimation: 610.16
#> Effective N for the ATT estimation:
#> treatment: 475
#> control: 156.68Note that the estimated coefficients except for est are
for the propensity score estimation.
Check covariate balance between the treatment and control groups
before and after the NAWT with cbcheck().
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
cbcheck(fit)
par <- oldpar # Just for adjusting plot marginsLet’s compare the inverse probability weights estimated by the nawt
with those estimated by the standard logistic regression with
plot().
plot(fit)
Finally, check the weights used in propensity score estimation and
distribution of the estimated propensity scores in the NAWT with
plot_omega().
plot_omega(fit)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.