Slide 4: the third point is sort of orthogonal to the fundamental problem of causal inference. I would drop it and simply work with the usual potential outcomes (fixed values rather than probabilistic draws) because it confuses the issue of causal inference with the issue of how to define a potential outcome.
Lukas responds:
Hm fair point. I do think it is an important point to discuss in the context of online experiments for e-commerce, since almost everybody in this space is running experiments to increase conversion (e.g. rate at which people purchase a product).
Don Green also writes:
Slide 8: it would be better if the example used the schedule of potential outcomes you provide in Slide 3 (e.g., table 2.1 in my book) because the audience is not expecting an example out of the blue. The simulated example is fine but should come afterwards.
Lukas responds:
Yes I've noticed a few times the audience gets confused at this point, so it's good you point it out.
I struggled with this, because eventually I want to use examples that use binary outcomes (like conversion), because that is what most people in the audience will be used to. However, I couldn't use a binary example for the preceding slides, because it leads to pretty nonsensical results with only six units.
I think I should add a new section about defining potential outcomes between slide 7 and 8, and update the preceding slides accordingly.
Don Green writes:
Lukas responds:
Don Green also writes:
Lukas responds:
I think I should add a new section about defining potential outcomes between slide 7 and 8, and update the preceding slides accordingly.