`library(RcausalEGM)`

```
if (!(reticulate::py_module_available('CausalEGM'))){
cat("## Please install the CausalEGM package using the function: install_causalegm()")
::knit_exit()
knitr }
```

Let’s first generate a simulation dataset with continuous treatment.

```
<- 1000
n <- 200
p <- matrix(rexp(n * p), n, p)
v <- v[,1] + v[,2]
rate = 1/rate
scale = rexp(n=n, rate=rate)
x = rnorm(n=n, mean = x + (v[,1] + v[,3])*exp(-x * (v[,1] + v[,3])), sd=1) y
```

Let’s take a look at the simulation data.

```
<-par(mfrow=c(1,3))
oldpar hist(x, breaks="FD", xlim=c(0,7), col="blue",xlab="x values")
hist(y, breaks="FD", xlim=c(0,7), col="red",xlab="y values")
boxplot(v[,1:5],main="First five covariates", xlab="Covariate index", ylab="v values")
```

`par(oldpar)`

Start training a CausalEGM model. Users can refer to the core API “causalegm” by help(causalegm) for detailed usage.

Note that the parameters for x, y, v are required. Besides, users can also specify the z_dims as a integer list with four elements.

(x_min, x_max) is the interval of treatment on which we evaluate the causal effect.

The key binary_treatment is set to be FALSE as we are dealing with continuous treatment.

```
#help(causalegm)
<- causalegm(x=x,y=y,v=v,
model n_iter=2000,
binary_treatment = FALSE,
use_v_gan = FALSE,
x_min=0,
x_max=3)
```

The average causal effect evaluated at the 200 uniform points in the interval (x_min, x_max) can be directly obtained from the trained model.

```
<- model$causal_pre
ACE boxplot(ACE, main="ACE distribution", ylab="Values")
```

We now compare plot of the true ADRF and the ADRF estimated by our model.

```
= seq(0, 3, length.out=200)
grid_val = grid_val + 2 * (1+grid_val)**(-3)
true_effect plot(true_effect, col=2, xlab="Treatment", ylab="Average Dose Response")
lines(ACE, col=3)
legend(x = "topleft", # Position
legend = c("True ADRF", "Estimated ADRF"),
lty = c(1, 1), # Line types
col = c(2, 3), # Line colors
lwd = 2)
```