Continous Treatment

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

Data Generation

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

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

Let’s take a look at the simulation data.

oldpar <-par(mfrow=c(1,3))
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")


Model training

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.

model <- causalegm(x=x,y=y,v=v,
                   binary_treatment = FALSE, 
                   use_v_gan = FALSE, 

Treatment Effect Estimation

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.

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

Plot of the average dose-response function (ADRF)

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

grid_val = seq(0, 3, length.out=200)
true_effect = grid_val + 2 * (1+grid_val)**(-3)
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)