Causal inference

Another one that comes to my mind is related to positivity.

So the idea that for valid causal inference, we need to have information about both potential outcomes, right? People, this type of person with treatment and without, and another type of person also with treatment and without.

What is your thinking about about positivity in this context.

I've tried to do some thinking about this.

We already have a handful of skills over there.

Bayesian Workflow, Causal Inference, and Amortized Inference, which was heavily written and influenced by Stefan Radeff.

And yeah, like the good thing also is that then the skills can, you know, mention each other and reference each other.

And so basically being a bit smarter when you're doing patient modeling and cousin modeling with AI agents.