[tuukkah]

TL;DR: Causal inference is a complex topic, not a simple tool.

How's the ice cream example better than the sugary snacks example given in the article?

Here's the part about needing to add more columns to the data:

> When dealing with a causal question, it’s crucial to include variables known as confounders. These are variables that can influence both the treatment and the outcome. By including confounding variables, we can better isolate and estimate the true causal effect of the treatment. Failing to add or account for confounding variables may lead to incorrect estimates.

[nl]

> How's the ice cream example better than the sugary snacks example given in the article?

Not the OP, but because that fails to explain how the basic hypothetical example works(!)

You want to know how much your sales would be in a parallel world where kids were stuck with bland snacks compared to your sweet treats. This is where causal inference steps in to provide the solution. (nice graph follows)

So how is that done?

[KRAKRISMOTT]

> TL;DR: Causal inference is a complex topic, not a simple tool.

The simple version using graphical models and joint probabilities isn't difficult to explain or teach. The issue is that to do anything useful with it at scale you either need MCMC or variational inference and that's an entirely different bag of worms all together. For medical datasets you rarely have "scale", instead you have very few sample cases and a large expert model (the doctor/specialist).