My backgrpund is in ux design and strategic planning and I’ve recently started learning about Causal ML (as part of learning ML).

I found that you already need to bring your assumption/reasoning of causation to the model as a flow chart, it’s usually more than A leads to B. Causal ML then uses your data to predict the accuracy of your assumption.

Here’s an obvious example:

If I have an A/B test of an ecommerce checkout, one blue and one red button. and in an a/b testing, red performs better. then a prediction model would learn that red performs better than blue.

In Causal ML, i would bring all factors in: background color, position, button color font, user & purchase information.

I can then create a first causal discovery model to come up with a network graph of the relation and then use causal ML to calculate the probability of effects.

Turns out, the color mages a difference for older shoppers because blue has lower contrast than red. so I could also choose another color with similar contrast for the same perfomance effect.

But I agree with you, a lot of that the thought work needs to be done before.