# ife [![CRAN status](https://www.r-pkg.org/badges/version/ife)](https://CRAN.R-project.org/package=ife) S7 class for influence function estimands with forward mode automatic differentiation for variance estimation. ## Installation You can install the development version of *ife* from [GitHub](https://github.com/) with: ``` r # install.packages("pak") pak::pak("nt-williams/ife") ``` ## Example Consider estimating the population mean outcome under treatment $E[E[Y \mid A=1,W]]$ and control $E[E[Y \mid A=0,W]]$ using augmented inverse probability weighting (AIPW). ``` r 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()`: ``` r 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: ``` r ife1 - ife0 #> Estimate: 0.254 #> Std. error: 0.042 #> 95% Conf. int.: 0.172, 0.336 ``` The multiplicative effect (risk ratio) can be estimated as: ``` r ife1 / ife0 #> Estimate: 1.583 #> Std. error: 0.129 #> 95% Conf. int.: 1.33, 1.837 ``` For 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: ``` r exp(log(ife1 / ife0)@conf_int) #> [1] 1.35 1.86 ```

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