$z = f(x, y)$

$x: Treatment$
$y: Covariates$
$z: Outcome$

   $\text{causal effect of x on z}$
$=\text{sensitivity of change of z with respect to x, with y held constant}$
$= \text{partial derivative of z with respect to x}$
$= \frac{\partial f}{\partial x}=\lim _{h \rightarrow 0} \frac{f(x+h, y)-f(x, y)}{h}$

In this repo, simulated data, denoted as $z = f(x, y)$, will be fed into a super learner to train a model. The trained model will subsequently generate values of $z$ within a specified range of $x$ and $y$ using Monte Carlo method. The expression $\frac{f(x + \Delta x, y) - f(x - \Delta x, y)}{2 \Delta x}$ will be used to compute the partial derivative of $z$ with respect to $x$.