# ctseff: Estimating average effect curve for continuous treatment In ehkennedy/npcausal: Nonparametric causal inference methods

## Description

`ctseff` is used to estimate the mean outcomes in a population had all subjects received given levels of a continuous (unconfounded) treatment.

## Usage

 ```1 2``` ```ctseff(y, a, x, bw.seq, sl.lib=c("SL.earth","SL.gam","SL.glm","SL.glmnet", "SL.glm.interaction","SL.mean","SL.ranger")) ```

## Arguments

 `y` outcome of interest. `a` continuous treatment. `x` covariate matrix. `bw.seq` sequence of bandwidth values. `sl.lib` algorithm library for SuperLearner. Default library includes "earth", "gam", "glm", "glmnet", "glm.interaction", "mean", and "ranger".

## Value

A list containing the following components:

 `res` estimates/SEs/CIs for population means. `bw.risk` estimated risk at sequence of bandwidth values.

## References

Kennedy EH, Ma Z, McHugh MD, Small DS (2017). Nonparametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society, Series B. arxiv:1507.00747

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```n <- 500 x <- matrix(rnorm(n * 5), nrow = n) a <- runif(n) y <- a + rnorm(n, sd = .5) ce.res <- ctseff(y, a, x, bw.seq = seq(.2, 2, length.out = 100)) plot.ctseff(ce.res) # check that bandwidth choice is minimizer plot(ce.res\$bw.risk\$bw, ce.res\$bw.risk\$risk) ```

ehkennedy/npcausal documentation built on Feb. 26, 2021, 2:43 a.m.