I've read this presentation about using a BART model to find out the causal effect of a certain variable with respect to a target variable (say, how much does a specific medicine actually helps treating a certain disease).
I'm still grasping the main and essential causal inference concepts, but I'm already familiar with the idea that in observational data, if we want to find out the average causal effect of a treatment, we need to make sure we're conditioning or stratifying our target variable according to some possible confounders. In the medicine example, that could be age, weight, etc. That is, we need to guarantee some form of conditional ignorability.
I also learned that there are structural causal models, which can help choose on which input variables we'll apply this conditioning. For example, there may be an input variable that is a collider and won't really help to find out the causal effect of, say, T -> Y.
In this cenario, I'm assuming that a non-parametric model such as a BART model could help estimate the ACE (Average Causal Effect) by calculating the intervention data (say, the counterfactuals) and then using that to estimate the ACE.
However, I'm not sure how one would use BART if you're not sure on what input variables you should condition. Does the model handle that?
In fact, in a more general approach, if I only have a handful of variables (e.g.: A, B, C, D, E, Y) and I want to find out if any of the A-E variables have a causal effect on Y, what should I do? Then, assuming there IS a causal effect between any of those variables, which one is the largest? How can I determine that?
If there's any solution to the questions I'm posing, is that solution possible to implement in R? Are there any examples of that?