This is also a strong argument for "laboratories of democracy" and local control -- if everybody agrees what to do then there is no dispute, but if they don't then let each local region have their own choice, and then we get to see what happens. It allows more experiments to be run at once. Then in the worst case the damage of doing the wrong thing is limited to a smaller area than having the same wrong policy be set nationally or internationally, and in the best case different choices are good in different ways and we get more local diversity.
Maybe we're at a local optima, though. Maybe this is a sign that we should just double down, surge on in there and get the job done by continuing to do the same thing and expecting different results. Maybe it's not the spec but the implementation.
Recommend a play according to all available data, and logic.
> This is also a strong argument for "laboratories of democracy" and local control -- if everybody agrees what to do then there is no dispute, but if they don't then let each local region have their own choice, and then we get to see what happens. It allows more experiments to be run at once. Then in the worst case the damage of doing the wrong thing is limited to a smaller area than having the same wrong policy be set nationally or internationally, and in the best case different choices are good in different ways and we get more local diversity.
"Adjusting for other factors," the analysis began.
- [ ] Exercise / procedure to be coded: Brainstorm and identify [non-independent] features that may create a more predictive model (a model with a lower error term). Search for confounding variables outside of the given data.
"Pete, it's a fool who looks for logic in the chambers of the human heart."
Logically, we might have said "prohibition will reduce substance abuse harms" but the actual data indicates that margins increased. Then, we look at the success of Portugal's decriminalization efforts and cannot at all validate our logical models.
Similarly, we might've logically claimed that "deregulation of the financial industry will help everyone" or "lowering taxes will help everyone" and the data does not support.
So, while I share the concerns about Responsible AI and encoding biases (and second-order effects of making policy recommendations according to non-causal models without critically, logically thinking first) I am very skeptical about our ability to deduce causal relations without e.g. blind, randomized, longitudinal, interventional studies (which are unfortunately basically impossible to do with [economic] policy because there is no "ceteris paribus")
https://personalmba.com/second-order-effects/
"Causal Inference Book" https://news.ycombinator.com/item?id=17504366
> https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...
> Causal inference (Causal reasoning) https://en.wikipedia.org/wiki/Causal_inference ( https://en.wikipedia.org/wiki/Causal_reasoning )