S7 class for influence function estimands with forward mode automatic differentiation for variance estimation.
You can install the development version of ife from GitHub with:
# install.packages("pak")
pak::pak("nt-williams/ife")Consider estimating the population mean outcome under treatment
library(ife)
# Generate simulated data
n <- 500
w <- runif(n) # confounder
a <- rbinom(n, 1, 0.5) # treatment (randomized)
y <- rbinom(n, 1, plogis(-0.75 + a + w)) # outcome
# Create data-frames for counterfactual predictions
foo <- data.frame(w, a, y)
foo1 <- foo0 <- foo
foo1$a <- 1 # everyone treated
foo0$a <- 0 # everyone untreated
# Fit outcome model and generate predictions
pi <- 0.5 # known propensity score
m <- glm(y ~ a + w, data = foo, family = binomial())
Qa <- predict(m, type = "response") # predicted outcomes
Q1 <- predict(m, newdata = foo1, type = "response") # under treatment
Q0 <- predict(m, newdata = foo0, type = "response") # under control
# Calculate un-centered influence functions
if1 <- a / pi * (y - Qa) + Q1
if0 <- (1 - a) / (1 - pi) * (y - Qa) + Q0 Create ife objects for these estimates using
influence_func_estimate() or ife():
ife1 <- influence_func_estimate(mean(if1), if1)
ife0 <- ife(mean(if0), if0)ife then allows you to estimate contrasts between estimates, with variance estimated using automatic differentiation. The additive effect (risk difference) can be calculated as:
ife1 - ife0
#> Estimate: 0.254
#> Std. error: 0.042
#> 95% Conf. int.: 0.172, 0.336The multiplicative effect (risk ratio) can be estimated as:
ife1 / ife0
#> Estimate: 1.583
#> Std. error: 0.129
#> 95% Conf. int.: 1.33, 1.837For the risk ratio, which is strictly positive, you can estimate the effect on the log scale and exponentiate the confidence intervals to ensure the lower bound is always positive:
exp(log(ife1 / ife0)@conf_int)
#> [1] 1.35 1.86