A company knowing or not knowing something is not binary. Reality is complex. Companies have plenty of communication about potential issues that don’t definitely demonstrate that a problem exists. A situation where a low-level employee has given information is not the same as when the information is known to company leadership. There’s a huge difference between the company knowing about the issue a decade before the “public revelation” and them knowing a month before it because the lead researcher asked them to look over their numbers.
Proving whether or not a chemical causes a given issue isn’t easy. The scenario where an independent researchers is able to provide a definite proof that a chemical causes a certain illness and a company never got any idea that there’s a possible link that could be investigated before the official publication seems to me not how these things usually play out.
Most of the time what we care about isn’t a binary outcome. When deciding whether or not to take a drug, we care a lot about the strength of the effect of the drug. Measuring effect sizes is important.
I mostly agree with this, principled and robust estimations of effect size are hard and also important. Maybe someday I’ll write a primer on Judea Pearl covering Bayesian networks and causal graphs, which in my mind is the framework that unifies these approaches, but that would take me a while and would require some more research, so I didn’t get into it here.
A company knowing or not knowing something is not binary. Reality is complex. Companies have plenty of communication about potential issues that don’t definitely demonstrate that a problem exists. A situation where a low-level employee has given information is not the same as when the information is known to company leadership. There’s a huge difference between the company knowing about the issue a decade before the “public revelation” and them knowing a month before it because the lead researcher asked them to look over their numbers.
Proving whether or not a chemical causes a given issue isn’t easy. The scenario where an independent researchers is able to provide a definite proof that a chemical causes a certain illness and a company never got any idea that there’s a possible link that could be investigated before the official publication seems to me not how these things usually play out.
Most of the time what we care about isn’t a binary outcome. When deciding whether or not to take a drug, we care a lot about the strength of the effect of the drug. Measuring effect sizes is important.
I mostly agree with this, principled and robust estimations of effect size are hard and also important. Maybe someday I’ll write a primer on Judea Pearl covering Bayesian networks and causal graphs, which in my mind is the framework that unifies these approaches, but that would take me a while and would require some more research, so I didn’t get into it here.